Over the past few years, Paul Rogers (an experienced ecommerce consultant at Vervaunt) done a lot of work with consulting relations (directly with traders) and Kelvu with e-commerce site search. At that time, I have seen a clear change in B2B retail, in which the customer experience is being concentrated with B2C Retail. Search is a major part of the purchase process and one B2B trader should not ignore.
In Paul Rogers experience, searched led conversion rates only based on users who have completed a search are basically 3-6 times higher than journeys without a search.
Also, for the B2B stores, this is higher due to the nature of the purchasing process. One argument that is based on this intention, but I have done two projects last year where we have used a high proportion of people to use the search and we have achieved incremental growth in revenue on both occasions.
Here is how the B2B merchants can harness the strength of search:
Serve Customer-Specific Results
The key features of B2B are the ability to assign pricing and product visibility to particular customers and customers groups. On the pricing side of things, this means that the displayed value to customers will be different from all products or only specific products, which will need to be adapted basically for search.
The same principle applies to customer-specific catalogs. You can show products which are set to be available to a specific customer in the search.
Support Tiered Pricing
Another feature which is mostly used by Business-to-Business traders is tiered pricing. Such as a discounting based on the number of items purchased. You can display the price based on a possibly lowest price of the item.
Provide An Rich Auto-Suggest and Faster Experience
To provide fast and auto-suggest experience is another vital need for lots of traders which can improvise the entire customer experience and incrementation in the speed of selecting products and eventually completing a purchase.
In my experience, permitting the customers to filter in this initial overlay gets a lot of value because most B2B stores have a big catalog. Another feature which can add value here and is often requested is ‘Quick add to cart’ button.
Use Merchandising Capabilities And Advanced Product Boosting
Usually, Business-to-Business stores have very big product catalogs which able to make rules to promote specific products and groups of products in some ways is very important.
Your search tools must be able to perform these tasks:
- Globally boost the visibility of a particular SKU
- Boost particular SKU visibility
- Globally Boost a particular selection of products(based on product attributes)
- Boost particular items for particular queries
- Assign hero SKUs for particular queries and groups of queries
Depending on how much complex is your client base and catalogs are. You may also have the potential to apply various boosting arguments to different users.
Many of these requirements will be similar for filtering. For example, depending on the specific questions or the ability to assign filtering orders to users, as well as allowing them to be machine-learned, the filters support the effectiveness of queries and overall search performance.
At least, the filtering needs to be customized based on the selection of returning products.
Another feature I think is that B2B merchants can be valuable for the both. Both the results page and overlay have the ability to serve banners and ad blocks as part of the search experience. Ideally, this will be manageable at a query and customer level and will support scheduling.
Users are likely to search on B2B stores by SKU, so this is the thing that needs support.
Reporting is a big segment of the search for all retailers because it can really provide a lot of information about what the user is looking for, as well as what is doing. To maintain a strong search solution. It is important that you are working and not working. Here are some basic reports that you should use:
- Search term reporting: Clicks and revenue-driving by the keywords (by location and devices)
- Use of Filtering: Reporting what filters are being initiated and the values being selected
- Refinements: Drill-down ability by location, device, and etc
- Zero-result search term: showcasing the errors which need to be handled through custom messaging, synonyms or redirects.
- Device: Search performance across device types: reporting
Your web analytics platform will run more fine data. In which searches are displaying on the site, behaviors on search results pages, further segment performance, and usage.
Machine Learning And NLP: Take Advantage
As I have touched so far in this article in detailed. Machine learning and natural language processing are able of adding lots of value to Business-to-Business merchants. Automatically by optimizing results based on performance and user behavior, and References from more capable queries.
The main advantage of learning the machine in search is that the results are either update in real-time either. Or example, promoting products that are altering better or getting more clicks.
More information about the benefits of natural language processing. More able to serve more robust results by understanding more about the users who are searching and not completely reliant on relations between products and conditions.
Both are need in modern-day search and they are likely to reduce manual overhead for your e-commerce and merchandising teams. In the same vein, catalog prosperity is another potential that can help in driving more accurate results. As well as process more complex search queries.
While talking about features that support the results of the better long tail, another important requirement will be to index more data and information, for example, size and value, and product reviews and Q & A content.