Amazon-like experiences for your auto parts e-commerce site give you an edge.
According to CNBC, while other areas of the economy add payroll, retail “is one of just two industries that have lost jobs over the last few years.” This reduction in jobs is the resulting outcome of consumers shifts focusing on online convivence and a focus on lower prices.
The automotive retail industry is no different. Amazon is expected to sell nearly $6.3 billion worth of auto parts, accessories and car care products in 2019. This number doesn’t include the projected $1.6 billion in replacement parts made by original equipment manufacturers (OEMs), bringing its total to almost $8 billion according to Hedges & Co. Of this total, mobile auto parts sales will be $7.5B in the U.S. in 2019.
How do you compete with Amazon for auto parts e-commerce?
Amazon, Walmart, and other players seem to have a considerable advantage in the e-commerce area. They have a significant platform available to find, sell, and fulfill parts to their existing customers. But their technology has limits. Typically, the best bet is to focus on the acquisition of traffic (and sales) from Google. But this has problems:
- You are always paying Google for this customer
- This approach fails to focus on the Total Lifetime Value (TLV) of the customer
If your customers are always going to Google in order to find a product on your site, you are paying for that traffic. If you fail to bid correctly, that next visit will go to your competitor. Part of your e-commerce strategy must rely on them continuing to go to your website.
With recent advancements in commercial software, particularly in artificial intelligence (AI) and machine learning (ML), the technology that Amazon has spent billions on is available for a fraction of the cost. This advancement allows you to incorporate these technologies within your infrastructure. Doing so can significantly improve your conversion rates both through the original sale and through recommendations, which lead to upsell and cross-sell opportunities.
What does this look like in simple terms?
Many retailers focus on traffic acquisition from product placements on Google.com. A search for a “zx10r cable-clutch” on Google’s shopping site show’s a result from Walmart.
Click on “visit site” and then run the search, and the product is nowhere to be found. Note that even the word ‘cable’ was added to assist.
So if you go to Walmart’s website, you will need to spend a significant amount of time and have some luck to be able to find this clutch cable. Given the trends of consumers, this is not likely to occur.
What capabilities you should expect?
With a power autocomplete, you can help suggest your users to highly probable search queries or refinements. Here you can see a search by the department based on the prediction that the user is looking for an iPod touch.
Query intent classifier
You can infer what the user is searching for (i.e OEM or VIN search) by classifying the search that the user submits. You can dynamically create relevant facet fields that contain categories relating to the query and tune the query based on that.
You can personalize and recommend items (i.e. other parts) that are related to the parts that they are looking at our related to the users’ behavior.
Would you like to see a compelling demonstration?
Contact us to schedule a demo and discuss how this can be implemented for you.