Perfect storm — Five technology trends may be coming together to revolutionize the aftermarket
More than 10 years ago, during a panel discussion at an industry event, in responding to a question regarding the importance of robust product data in the supply chain, I made the controversial comment that “the data regarding the product was almost more important than the product itself.” Another panelist, whom was is a highly respected industry executive, quickly remarked, “No, it is the product.”
We both were right! His very valid point was that if you didn’t have a quality product, no one would buy it. My point was that without robust product data content, regardless of how good the product was, the consumer would never be able to find your product to purchase it. The takeaway was that “content-driven marketing” had reached the automotive aftermarket and would so result in a big leap forward in revolutionizing the way our products would be distributed and sold.
Today, content-driven marketing quality and completeness is a given. However, we have several business technology trends that are combining, which may form the basis for the next “big leap revolution.” These include:
- The continued success of content-driven marketing within the automotive aftermarket
- The continued growth and exploitation of robust data catalogs and data lakes
- The move toward data-driven decision making & data-driven marketing
- The adoption of AI and machine learning – smart data catalogs
- The ability to quickly implement efficient agile business processes
The road to content-driven marketing and its impact on product distribution in the aftermarket
The Content Marketing Institute defines content-driven marketing as “a strategic marketing approach focused on creating and distributing valuable, relevant and consistent content to attract and retain a clearly-defined audience — and, ultimately, to drive profitable customer action.”(1) However, when the industry began this journey, it has a much simpler goal.
About 25 years ago, the Auto Care Association, then called APAA, initiated a project to set standards for select attributes to define a common language, if you will, so that trading partners’ computer systems could talk to and understand each other. It addressed such things as “what do you call a Chevrolet — a Chevy, Chev, Cv or Chevrolet.” By establishing a standard reference that supply chain trading partners could map their internal terms to, computer systems could begin to talk to each other.
From these humble beginnings, today’s powerful and robust Aftermarket Catalog Exchange Standard (ACES), the North American industry standard for the management and exchange of automotive catalog applications data was born. In a recent article, Brian Albright summarized: “ACES formatting not only makes it easier for buyers to find the correct parts but also helps improve communications across the supply chain during these transactions. Standardization also makes it easier to execute e-commerce strategies.” (2)
With the same humble beginnings, in 2004, the Auto Care Association Product Information Exchange Standard (PIES), an equally robust and thorough standard for the exchange of product information for the automotive aftermarket industry was born. In addition to ACES applications data, the PIES standard enables the transfer of product attribute information on thousands of product types in the industry.
Together, these standards, along with real-time inventory availability information, became an enabler to move the industry toward Content Driven Marketing.
During this process, the automotive aftermarket experienced several “big leaps” in its ability to market and distribute its products. Some of these include:
1. Speed to market
Prior to the standards being generally adopted, the transfer of application and product information was largely a manually intensive process. It was normal for it to take 60 to 120 days from the time are new part was released from a supplier until it was available in data catalog and ordering systems. Thus, this process would cause significant delays in the time it took a new product to reach the market place.
Post-standard adoption, the process of publishing new applications and product data could be automated, thus greatly reducing the time to market. In some cases, a supplier could release a new product or application at 8 a.m. in the morning and receive their first order the same day.
2. The role of data warehouses
Parallel to the adoption of the standards, the industry saw the rise in third-party data warehouses. Essentially, these services allowed manufacturers and suppliers to centrally house their data and make it available to their customers. The advantage of this business model for the suppliers is that the data warehouse would take on the responsibility of the data distribution process. That is the delivery of high standard quality product and application information to their resellers anywhere in the supply chain. Suppliers would no longer need to manage multiple data receiver requirements, each of which was specific to each organization. Suppliers could now focus on improving the quality of the data content and how to use that content to improve sales.
By centralizing the data storage of multiple supplier’s product and application information, the data warehouse advantage to the resellers a single source that could provide high-quality data for multiple brands in the format they needed. Thus, the data warehousing model provided significant efficiencies and data management cost reductions throughout the industry.
As the data requirements became more complex as content-driven marketing matured in the market place, the data warehousing business model also matured and evolved to meet these requirements. Being able to support search engines such as Google and complex requirements from third-party market places such as Amazon and eBay became common requirements.
3. Top of the search
By being able to provide robust and accurate product content to the market place, industry players could now move easily leverage search engines such as Google, Bing, etc. to improve sales. Content such as enhanced descriptions, images, videos, etc. all helped searches rise to the top of the results compared to those listings that were not as complete. The same was true with retailer instore data catalogs, warehouse and distributor part look-up system, shop and repair management system, etc.
4. Ecommerce – omnichannel & multi-channel – third-party marketplaces Robust content also facilitated the easy entry for industry players in all levels of the supply chain to expand their distribution channels to multiple levels in the supply chain. This is especially true in the ecommerce, direct marketing and third-party market places.
5. Support the ability to sell products not in stock
Complete and accurate product information coupled with inventory availability and order fulfillment speed allows retailers, etailers, and WDs to move inventory back up the supply chain to the suppliers and manufacturers. This allow these organizations to reduce inventory carrying cost while offering a larger selection to their customers. Dropship fulfillment models have become a mainstay of the ecommerce industry, including third-party market places.
The move to data-driven marketing and data-driven decision making
Wikipedia defines data-driven marketing as “a process by which marketers gain insights and trends based on in-depth analysis informed by numbers. Data-driven marketing refers to strategies built on insights pulled from the analysis of big data, collected through consumer interactions and engagements, to form predictions about future behaviors. This involves understanding data already present, data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience…” (3)
While the content marketing business model is one of the foundational requirements for the automotive aftermarket to move to data-driven marketing focus, the current data warehousing and data catalog modeling structure need to evolve further.
In the recent Bright Talk webinar, “The Unstoppable Rise of the Data Catalog,” Matt Aslett, VP AI & Analytics of 401 Research describes current data warehouse data modeling as driven. That is, you start with the answer, select the data and then do the modeling. Finally, you write the query and get the result. They are very good at answering the questions you knew about when you started. (4)
He goes on to discuss that a data-driven approach focuses on finding the right data to address the business opportunity or problem. Using an agile approach, you start with all the available data there is, explores that data, analysis, and query the data, model the data, query the data again, and views the results. Common, multiple data sources, both internally and externally, will be used and leveraged during the process.
The big advantage of this approach is that you are not limited to starting with the answer, but rather you are searching for the right questions. This approach allows you to “discover” solutions or business opportunities from the analysis of the data previously unknown. However, the movement to a data-driven marketing or decision-making model implies the evolution of a smart data catalog approach.
An oversimplified distribution example — forward inventory positioning a replacement part:
1) You begin with your forward inventory deployment plan or strategy.
2) For a given replacement part, using your content marketing data catalog, your analyst identifies what vehicle and vehicle applications the part can be used for.
1) Using an industry source, you access vehicle registration data to develop a geographical map, including numbers of where the vehicles are located in the United States.
2) Based on this vehicle distribution, update your forward inventory deployment plan for this part number….but wait!
3) You another industry source, you access the vehicle repair data by location, by time frame, for the above-identified vehicles. In general, this confirms your inventory deployment plan.
4) However, the data also shows a spike in accidents for the vehicle for a 90-day period in Colorado and Utah every year. When factoring this previously undiscovered accident trend, further analysis shows that increasing your forward inventory during this time frame in Utah and Colorado, will result in a 35 percent increase in sales for that region. This increase is a result of preventing out of stock occurrences for the 90-day period.
5) You adjust your forward inventory deployment plan to reposition inventory to the region.
In this example, your analyst accessed different data repositories, discovered a previously unknown demand trend, and increase sales in the region without increase overall supply chain inventory levels. While this is a very simple example, the same principles with and discipline within an organization and industry.
While this example shows the power of data-driven decision making, to efficiently implement this strategy, the move to smart data catalogs, that is, machine language augment data catalogs, is necessary.
As pointed out in a recent Gartner white paper as a recommended first step, the leveraging of “an ML-augmented data catalog as a first step in metadata management to simplify and, in some cases, automate the process of discovering, inventorying, profiling, tagging and creating semantic relationships between distributed and siloed data assets, which are becoming impossible to catalog manually.” (5)
Impact of AI and machine learning
“The explosion of content has outstripped the human capacity to process and synthesize even a fraction. Machine learning holds the promise of unlocking the value inside all your content – increasing employee productivity, improving customer experiences, accelerating business processes, and migrating risks.” (6)
In the example above, it might take a human analyst the better part of a day to complete the analysis, find the hidden trend, and recommend the changes in inventory deployment. And that is, of course, if the analyst spotted the hidden trend.
This is where machine learning (ML) and artificial intelligence (AI) comes in. Imagine if you could repeat the analysis in the example for 10,000 parts with multiple internal and external data repositories in a matter of hours. It may uncover hundreds of different hidden trends that may indicate new inventory deployment mythologies you have never considered. Using AI and ML algorithms and techniques may also allow you to react almost instantly to changes that impact your deployment.
AI and ML allow your organization to automate processes and discover possibilities using the data assets currently available in a manner that is unattainable without them. The speed of these techniques coupled within technology trend above will for the form the basis for the next “big leap” in the automotive aftermarket.
The implementation of ruthlessly efficient agile business processes
In any discussion such as this, you cannot forget about implantation.
In a recent article, Scott Luckett, Vice President, Industry Strategy for GCommerce Inc., stated: “I wrote about a business recipe for success in selling to, and through, online marketplaces. The major ingredients in equal parts are 1) great product content, 2) inventory visibility, and 3) ruthless efficiency of business processes.” (7)
The development and implementation of agile and efficient business processes is the final step needed to achieve the next “big leap.” Companies that need to use data to run and grow their everyday business will continue to increase at a fantastic rate. The impact of data-Driven decision making and data-driven marketing will exponentially decrease the time it takes to identify new opportunities. However, the corresponding analytical processes, agile business techniques, and efficient business processes will need to be implemented quickly for an organization to take advantage of these new opportunities. As always, speed and efficiency will be the key. The good news is that many organizations have already implemented methodologies which can achieve these goals.
Footnotes:
1. https://contentmarketinginstitute.com/what-is-content-marketing/
2. “DATA FORMATTING COULD HELP UNLOCK OE PARTS SALES” By Brian Albright. Wednesday, July 31, 2019, SearchAutoParts.com, Aftermarket Business World. https://www.searchautoparts.com/aftermarket-business/automotive-aftermarket-technology/data-formatting-could-help-unlock-oe-parts-sa
3. https://en.wikipedia.org/wiki/Data_driven_marketing
4. “THE UNSTOPPABLE RISE OF THE DATA CATALOG,” a brighttalk.com webcast by Matt Aslett, VP AI & Analytics, 401 Research & Aaron Kalb, Chief Data Officer, Alation, August 29, 2019.
5. “Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders” by Ehtisham Zaidi, Sr. Director Analyst, Gartner and Guido De Simoni, Sr. Director Analyst, Gartner, September 12, 2019 - ID G00394570. https://www.gartner.com/doc/reprints?id=1-1OI94B0U&ct=190917&st=sb
6. “Unlocking the value content of machine learning,” “Want to become a digital business.” Box.com
7. “IF YOU DON’T HAVE A STRATEGY TO GROW YOUR BUSINESS BY 15 PERCENT, THEN YOU WON’T” By Scott Luckett, Vice President, Industry Strategy for GCommerce Inc., July 29, 2019, SearchAutoParts.com, Aftermarket Business World. https://www.searchautoparts.com/aftermarket-business/opinion-commentary-distribution/if-you-dont-have-strategy-grow-your-business-1
