The most common use case for a Product Information Management (PIM) tool portrayed by those in the industry is all about syndicating data, whether that is to internal or external repositories. Getting data into an ecommerce platform, onto Amazon, into marketplaces, or any other sales channel is seen as the primary value statement. Although there is significant value in syndication to the sales systems, this limited view misses the point of a Golden Record for product: The entire customer journey must be considered from the customer’s perspective instead of just how to get data onto websites.
Today we’re going to walk you through that sales cycle to help you understand how to maximize your PIM platform and your product data.
Step 1: Inspiration to Buy
The traditional view on product data assumes that the content within your PIM becomes valuable when a customer is ready to make a purchasing decision. This view is short-sighted, as the customer journey begins when a customer for your products first identifies a need for those products. Whether that is based on self-identification or inspiration through advertising in any number of channels, the product data to support an inspiration moment is vital to consistently driving that inspiration to the correct products.
Inspiration utilizes product data in many different ways. It may be the keywords required to match a search engine or marketplace search to your products. It may be using the complete and accurate product data to support advertising to generate an inspiration moment. Regardless of how that moment is generated, product data must be there to support advertising, search, SEO, and product placement. This data is different than product data for sales, as it has to key in on use case matching, marketing information, and inspirational content. Ignoring this stage of the customer journey makes it much more expensive to acquire customers, as it assumes the customer journey begins with the sale.
Step 2: Research on What to Buy
Although spontaneous purchases still occur at the inspiration level, many technical products or higher-valued items still require some level of research to ensure the customer is choosing the right make, model and variant. This step can occur in two ways: The customer reviewing content on the manufacturer’s website, or the customer reviewing product information on a marketplace. This is where below-the-fold content becomes important, as it gives the customer a vision of how they could use the product and the value statements that product will provide.
Failing to build A+ content or below-the-fold content in a PIM tool leads to unscalable processes, brand messaging that misses the mark across sales channels, and customers making decisions in an absence of good content. This leads to missed sales opportunities, site abandonment, and increased returns as customers attempt to fill in the blanks for the value statements missed during this phase of the customer journey. Missing a sale is a problem, but so is not being able to keep a product that has already been sold in that customer’s possession.
With returns across all industries over 15% and making up close $1 Trillion USD, ignoring targeted product data for the research phase is a large cost for every company. Using product data won’t eliminate these costs, but it will help reduce the metrics for missed sales and costly returns before the customer makes a purchase.
Step 3: The Sale (The Decision to Buy)
The sale is the most talked about and documented stage of the customer journey. It is also the least understood from a product data perspective. Where the previous two stages set the stage for the purchase, the sales stage is all about limiting the friction in the actual sales process. This way, many of the elements of the sale are set up in the prior two stages. The customer knows the product they want and knows where they are going to buy it. This phase is mostly transactional, and as such has more to do with inventory availability, delivery requirements, shipping, and price.
It also comes with an understanding that the customer may research a product in one channel and buy from another. I might go to a power tool manufacturer’s website to find the cordless drill I want to purchase, but I’m going to check Amazon to find out the price and the delivery times before I buy. Unless the manufacturer makes a compelling offer on their website, I’m likely to use my Prime account to buy the product.
Ensuring the right product data to enable this sale is present in every possible channel the customers may purchase from is paramount to securing that sale. If a product is out-of-stock on one site, will take too long to deliver on another site, and is too high-priced at another site, the customer is likely going to re-evaluate their purchase decision. Therefore, this stage is all about ensuring your product data is up-to-date on all possible sales channels to make sure customers can purchase your products where they shop.
Step 4: Support
Have you ever purchased a product and then immediately had to find a YouTube video on how to install or use that product? Have you ever bought a product, thrown out the box, and then realized you threw out the users guide with the packaging? How many times have you tried to repair a purchase weeks or months after buying it only to realize you had no idea what parts you actually needed?
Support is forgotten in the product data world. Everyone assumes that a knowledge base or an FAQ document is sufficient to help the customer through assembly, education, and repair of a product. It’s not. There is a need to ensure that the product data, which includes assembly instructions, usage videos, repair guides, and warranty information, is stored in a manner that the customer can access without the traditional friction seen today.
AI is vital to the future of support, with chatbots and service bots coming online at a brisk pace. However, without the proper product data, relationships to service parts, and all the digital content (Warranty Documents, Installation Guides, etc.) to support AI this investment will not function properly. Building product data that supports your Customer Service teams as well as your AI is critical to the future of your service organization
Step 5: Disposal and Replacement
It is inevitable that a product will need to be disposed of, and potentially replaced. Many people look at this as a sustainability issue, but it is more than that. People just don’t want to know what there are sustainable ways to recycle or dispose of a product in the future: They want to know how to dispose of the product they bought 5 years ago that reached it’s end of life today. Having this product data available isn’t just necessary during the sale: It’s necessary to keep your customers informed in the future.
It also leads to the next inspiration moment. Although only about half of the products consumers buy are considered impulse purchases or purchases that won’t be repeated, B2B customers typically refill the same order when a product runs out. Whether this is a consumable product or durable equipment, they will look to where they originally made that purchase to guide them on how to replace that item.
Unfortunately most industry professionals see this only as a sales activity. By ignoring the rest of the customer journey they are ignoring the potential for repeat sales and higher customer retention. Most Ecommerce platforms and Content Management Systems have the potential to meet this customer need, but the data simply isn’t maintained in PIM in a way to support those systems. This misses opportunities for repeat sales and brand loyalty that turn one-time buyers into brand advocates.
The Whole Customer Journey is Important
Opening up your product data practice to the concept of servicing the entire product journey can be difficult. Many companies struggle with being able to syndicate to the sales phase, let alone understand the differences in the 4 other phases. However, building a product data program that looks at every customer touch point, from inspiration to obsolescence and replacement, can change your business from a company that chases every sale to a company that dominates market share in your industry.
This customer-centric view also changes the dynamic from a platform’s functions supporting a data need to the data supporting the customer journey. This is important, because just supporting data needs is a reactionary methodology, while supporting the customer journey is designed to be proactive. The companies always reacting are the slowest to innovate, which reduces margins, increases returns, and requires constant projects to course correct every time a new need arises.
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Contact us today if you want to find out how to maximize your customers’ journeys while maintaining margins and reducing returns.