Gartner Magic Quadrant for Search and Product Discovery<\/a>, it\u2019s clear that most think of it as product search. But organizations are missing the boat (one full of potential customers by the way) if they narrow it down to just what happens on a site\u2019s search bar.<\/p>\nThat\u2019s because searching is entirely different than discovering. While search can be part of discovery, it\u2019s a slice of it. While it may be a big slice right now, new technologies and strategies are expanding the way consumers discover products and the way companies cater to these behaviors. By not embracing this new wave of discovery, businesses limit the ways in which they can surprise and delight customers.<\/p>\n
But if this larger universe of product discovery is so promising, why aren\u2019t more companies venturing into it? What barriers are keeping organizations from transforming their search-centric experience to full-on product discovery?<\/p>\n
If you guessed data, you are correct. But perhaps not in the way you think.<\/p>\n
Why most B2B companies aren\u2019t getting B2B product discovery right<\/h2>\n Most organizations have a lot of product data (if you think of data as discrete product attributes). They have the description, size, weight, dimensions, and a long list of other technical specifications for each of their thousands of SKUs. If you look at a typical online search experience, you\u2019ll see this data is available to consumers, mostly in spec sheets on product pages or in filters on search results pages, like this one:<\/p>\n<\/p>\n
There\u2019s lots of data, but not much of it is useful to consumers.<\/p>\n
For example, one way to filter results in the example above is by \u201cHeight to Top of Door Hinge.\u201d Odds are that most, if not all shoppers don\u2019t know or care about this measurement. It\u2019s certainly not how people shop.<\/p>\n
This is a snapshot of a bigger problem\u2014while brands lots of product data, it\u2019s not in a format that connects with the needs of customers or the way they discover products online.<\/p>\n
This problem can be traced back to the dawn of ecommerce, all the way back in the early 2000s. This is especially true for B2B experiences.<\/p>\n
In the early Aughts, I worked with several large B2B organizations that were experimenting with online purchasing experiences. These organizations were leading the charge in B2B ecommerce, driven mainly by the goal of reducing the burden on their customer services organization. But those early experiences were essentially a window directly into the ERP. In one actual example a customer could buy a tank car of polyethylene, request a delivery date, get availability data, and make a purchase without a single call to customer service. Any product data displayed came right from the ERP and looked a lot like this:<\/p>\n
As experiences matured and scaled, direct access to the ERP became less ideal. This brought about the rise of product information management (PIM) systems. PIMs allowed organizations to centralize product information and share it across enterprise solutions and departments. But that product information still looked like the data in the ERP. It was made up almost entirely of structured, attribute-driven elements suited much more to internal use than for buyers, like in the below example:<\/p>\n
This product information was published on an ecommerce platform, where it was supposedly \u2018enriched\u2019 for the customer experience. But that enrichment was very limited, very manual, and very much still driven by technical attributes. You could manage variations with colors and add media, but that was it. This early, ERP-centric approach is what has shaped B2B ecommerce over the past 20 years. This highly structured data powered early, keyword-focused search experiences that required customers to know very specific attributes to find what a product they needed.<\/p>\n
It was around this time that I started hearing an objection that\u2019s become the primary barrier to B2B ecommerce, \u201cMy business and products are too complex to be online.\u201d<\/p>\n
These organizations relied, and still do rely, almost completely on their sales and customer service teams to drive sales. The prevailing wisdom is that only a human being can handle the immense task of diving into a 10,000 SKU catalog of products that can be configured a million different ways and come back out with the exact right set of products for a customer\u2019s needs.<\/p>\n
\n
\n\t\t\t\t\t\t\t\t You can\u2019t digitize a salesperson\u2019s knowledge and experience.<\/p>\n
But what if you could? How would you do it? What would be the formula you follow to recreate a great salesperson online?<\/p>\n
How to nail B2B ecommerce: What it takes to create the ultimate online salesperson<\/h2>\n Let\u2019s start by defining what makes a great salesperson. There are typically three characteristics that elevate one from good to best:<\/p>\n
\nThey know everything about their products: They know what\u2019s in the catalog, how each one fits together, and all the technical attributes. But they also know how these technical attributes map to use cases, industries, environments, and personas.<\/li>\n They know everything about their customers: They\u2019re skilled at asking questions, getting to know who their customers are, and what they need.<\/li>\n They know how to connect the right customer to the right product: They use everything they know about their customers\u2014all their needs, history, and unique use cases\u2014to recommend the best products for specific buyers. Then they explain why the products are the best fit and answer any questions.<\/li>\n<\/ol>\nThese are the three elements that B2B companies must replicate in their online experience. It\u2019s now that we come full circle back to the primary barrier we talked about at the beginning of this article: data.<\/p>\n
Product data is too technical, too complex, too big, and too scattered for most B2B organizations. Manually enriching this data to make it useful for a true online buying experience would take months and entire teams. By the end, much of the data would be so outdated you\u2019d just have to start all over again.<\/p>\n
What\u2019s the solution?<\/p>\n
An investment in systems that focus on clean, standardized, and enriched data as a foundation for great ecommerce experiences. A system that treats product data not just as an extension of spec sheets and ERPs, but as an answer to a customer\u2019s needs or problems. A system that uses data to build experiences that make products discoverable, configurable, and purchasable online.<\/p>\n
These systems do exist<\/a>, and investing in one should be a top priority for B2B companies that want to reduce the cost of sales while increasing the number (and quality) of leads that come in the door to ultimately grow revenue.<\/p>\n
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