The Aftermarket is e-Commerce
Recently, Hedges and Company reported that auto parts sales directly influenced by digital and e-commerce will increase to $152 billion by 2021. Modern e-commerce is more than just having a storefront is all about search. Industry research is suggesting that there are at least 70 million aftermarket products searches every month with Google. A majority of this activity for aftermarket auto parts is business to business (B2B).
Google maybe helps with the first step in acquiring a customer, visibility of your storefront or marketplace. The real challenge is making sure that your site provides an experience where potential customers find the product that suits their needs and the convert on that initial visit. This successful experience is key to warranting a customer’s return visits.
When conducting e-commerce in the aftermarket space, there are many challenges, including:
- The data flows from your back-end databases to your frontend commerce system
- The difference between SKU formats
- The many attributes that make up a part driving application/fitment
- The differences between internal and external nomenclature
An often-overlooked benefit of search on your e-commerce store is that you are collecting some feedback on every search query. Customers when conducting searches are telling you exactly what they are looking to buy. This feedback combined with other signals you are already capturing in your web analytics you can create a personalized experience. These personalized experiences are crucial to e-commerce. Historically, attempts at personalization are implemented through user segmentation by defining buyer or visitor persona. This approach fails in two key areas: 1. Scale and 2. Individualized experiences. Creating user personas is a time-consuming process that requires a great deal of effort to implement and maintain. In the end, you end up with broad user segmentation based on limited data and assumption.
The Significance of Machine Learning in e-Commerce
You have probably heard of AI. Machine learning (ML) is a branch of artificial intelligence (AI) focused on using algorithms to make predictions and recommendations based on a dataset. In e-commerce, the data set is user behavior as signals for making recommendations. This ML powered recommendation capability lets you improve the customer experience and increase revenue at the same time.
Machine Learning powered recommendations have penetrated mainstream online retailers adjusting visitor expectations. Website visitors expect personalized recommendations and offers from the sites they visit.
Do I need Machine Learning (ML) for aftermarket auto parts?
The short answer is yes. Your user’s past behavior is an excellent predictive measure of their future behavior. This behavior as we previously mentioned generate signals that you can observe. Some signal behaviors include:
- Past Searches
- Results Clicked
- Navigation Paths
- inbound Referral
- Geo Location
Better user experience with Machine Learning signals
Feeding this signal data to a Machine Learning system allows you to deliver a better user experience by showing results to customers based on more than simple relevancy. Machine Learning for your Aftermarket auto parts stores will enable you to:
- Understand what the user is looking for even when they misspell a query
- Offer recommendations based on their behavior leading up to the search (pages visited, etc.)
- Offer recommendations based on other users’ behavior (what did other people end of buying previously)
- Provide dynamic offers or featured results
- Inspect their query and prefilter searches (i.e., year, make model)
- Suggest navigation category based on what they a searching
- Auto tune of the system improving keyword relevancy (Learn to Rank)
Example Auto Tuning from Head to Tail Analysis
Providing a recommendations-based experience
Refining search with signals is a significant first step to improving your customer experience. Providing great search is only one facet of a robust e-commerce solution, working best serving customers visiting your store with a clear idea of what they want to buy. The more significant opportunity for aftermarket platforms is to provide customers personalized browsing experience. Your storefront must leverage merchandising tools for recommendation and intelligent product browsing. These smart merchandising tools offer you the ability to have an Amazon-like personalization using ML models trained by all past shopper behavior (signals) to personalize the browsing experience of the next shopper.
MC+A solution for Aftermarket Auto Parts
MC+A has over ten years of experience in implementing search and machine learning. We also have a quite a bit of knowledge of the aftermarket vertical and have developed a solution that can really jumpstart the implementation of ML for your part catalogs. This solution starts with a partner technology, Lucidworks Fusion and combines it with MC+A created ML models to accelerate your time to market to provide your customers with a personalized experience that will improve customer satisfaction and increase revenue.
The Lucidworks Fusion Platform is a great foundation to this solution, because:
- It is built on Apache Solr & Apache Spark
- It uses applied Machine Learning (ML) to let you deliver hyper-personalized results
- It is proven at scale on the world’s biggest e-commerce websites
We’ve taken this technology platform and tailored it to the aftermarket space in a ready to run solution that has the flexibility to run in the cloud or on-premise via our managed services.
Machine Learning can increase revenue and improve your customers user experience. Our team would be happy to talk through the technology possibilities with you.
Get Help from the Experts
MC+A has the technical and business vertical expertise to deliver successful implementations of ML for your Aftermarket Catalog Search.
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