If you ever feel like your quoting process is long, inefficient, and inconsistent, just hop on Reddit —you’ll feel less alone.
“The turnaround time for us is anywhere from 48 hours to 3 weeks,” said one Redditor about their time to quote.
“We shoot for between 1 and 2 weeks…but [in my opinion], we should be spending more like 3 or 4 weeks,” said another in a different thread on the r/Manufacturing board.
But the most relatable quote might be this one, “This is what I do for a living I can tell you that it depends on a few factors.” Relatable because you can probably hear yourself saying this to a buyer before launching into the layers of complexity that goes into quoting the average order.
All these responses point to three hard truths:
- Quoting takes a long time and eats away at your team’s time, resources, and profitability
- The ‘it depends’ nature of quoting makes it nearly impossible to forecast and strategize effectively
- It’s really hard to solve these problems and reduce the time it takes to create accurate quotes
This article is all about tackling this last point and helping your team use product discovery tools to cut its time to quote.
What is product discovery for manufacturing companies
Product discovery is the process of finding a product that matches your needs and wants. This can take many forms depending on the person and where they are in their journey as a buyer.
In an example from the consumer world, a shopper might need new running shoes. Their product discovery path might look like this:
- Use ChatGPT to find out what kind of shoe is best for their habits and goals
- Search broad terms on Google (ie. Where to buy waterproof running shoes)
- Click on search results for brands they know and like
- Search on brand sites (ie. Waterproof running shoe)
- Use filters to narrow down choices and look at several product pages
- Read and watch reviews of shoes
- Go into a nearby store to try on the shoes
This path likely looks much different for B2B buyers and might include:
- Calling a sales rep of the manufacturer
- Searching a manufacturer’s website for the products they need
- Logging into their company’s purchasing system and assembling an order
- Talking to colleagues and peers
- Using third-party vendors to source products for them
The other twist for manufacturers is that it’s not only buyers going through product discovery. Sellers often also engage in some form of product discovery as they configure, price, and quote for customers.
Five product discovery tools to reduce time to quote
Product data enrichment solutions
Every good sales experience starts with good data. Imagine your sales team couldn’t find their sales sheets or the product catalogs they use were missing critical information. They’d be unable to create quotes without considerable time and effort.
The same goes for online experiences. If your product data isn’t centralized, clean, standardized, and enriched, you might as well post your 200-page product catalog and hope customers can make sense of it.
This is why the first product discovery tool you need in your tech stack is a product data enrichment solution. This kind of platform allows you to collect product data from various systems (ie. PIMs and ERPs) and convert it to data that can be used in sales conversations or a self-serve online experience. Here’s what that would look like:
- Clean: No errors, duplicate information, or missing values
- Standardized: Values are normalized across products
- Enriched: Product specs are connected to use cases and product relationships are mapped
Clean, standardized, and enriched data is the base of every solution below. It ensures you can create product discovery experiences that work and that they produce accurate results for quotes and RFQs.
Hybrid search
Only 49% of industrial companies have a search bar on their sites, according to The 2024 State of B2B Ecommerce Report by Forrester and Zoovu. That’s because it’s hard for a search engine to give accurate results for the wide range of queries that B2B buyers tend to make.
For example, your customers might search by:
- Model number
- Make
- Measurements
- Use (Ie. Scratch-resistant lens, gloves for handling chemicals)
- Compatibility (ie. Pump that goes with ACME 5000)
In the above list, there’s a mix of specific terms (model number) and subjective terms (compatibility). Guiding customers to the right product through the search bar, with these variables, is difficult.
Fortunately, a hybrid search solution is good at both rules-based queries (showing results based on specific parameters) and subjective queries (showing results based on intent and interpretation). Customers can identify the products they need quickly and either purchase them (eliminating much of the quoting process) or request a quote that’s already half-built.
Guided selling assistant
Every sales team has a standard list of questions they ask potential customers. Typically, salespeople take the answers, consult technical experts in the organization and comb through sales sheets and product catalogs, to build a quote. A guided selling assistant can do this work in less than two minutes.
A guided selling assistant uses a set of questions and user answers to recommend the best product(s). The question flow is often dynamic, changing with each answer to ensure recommendations are exactly what the buyer needs. It’s like having experts from the sales, service, support, and technical teams rolled into a single assistant that can help hundreds of customers at once.
The power of a guided selling assistant isn’t limited to a self-serve online experience. It can be added to your back-end sales system and used by salespeople on buyer calls. You can produce an accurate quote in a single call, validate it quickly, and turn every salesperson into an expert in the eyes of your buyers.
RFQ builder, BOM builder, and product configurator
Complexity kills quotes. Your sales team might be selling thousands of products with millions of combinations. Creating that perfect bundle over the phone or on a website can be a long and frustrating process for everyone. Plus, adding just one wrong SKU can destroy the entire quote.
But what if your salespeople and buyers could be guided through the RFQ process or assemble a bill of materials in a few minutes using an interactive tool? It would take a lot of the guesswork and anxiety out of the experience.
That’s what product configurators promise. With a visual, pick-and-choose interface, product configurators help customers build their own BOM and submit it for a quote. Product options change depending on needs and SKUs already selected to ensure everything on the list is compatible.
Product configurators can also plug into back-end CPQ software so a salesperson can build BOMs and quotes while talking to a customer and without having to consult anyone else or flipping through product catalogs.
Product advisor
No one spends hundreds of thousands of dollars without doing their due diligence. So of course your buyers have questions. When salespeople need to hunt down the answers to very specific and technical questions, or when buyers can’t find them quickly themselves, the sales cycle jumps up another few days. Or worse, the deal is lost before you even know it exists.
Fortunately, digital product advisors can tackle questions about products just like a veteran salesperson would. These product advisors use generative AI to search your product data and content resources (like manuals, spec sheets, and help articles) to answer questions that buyers have, whether it’s in a call with a salesperson or on a webpage.
These aren’t your run of the mill chat bots. These product advisors act more like gen AI tools (ie. ChatGPT). They understand context, intent, and subjective language to educate buyers about products at one of the most critical parts of their journey. They can also ask questions back to the buyer so they can provide the right information and ensure they get the most relevant and accurate responses.
Obstacles to adopting product discovery tools and how to avoid them
It’s no secret that today’s B2B buyers expect the same self-service options they enjoy in their personal shopping experiences. However, most B2B websites are still the static sales catalogs of yesterday. When buyers are not met with fast, personalized, and intuitive journeys, purchasing decisions slow and quote times grow.
The root of this problem stretches back to the early days of ecommerce when B2B companies focused on reducing service demands instead of building buyer-centric experiences. Relying heavily on technical data directly from ERPs and PIM systems, they created search experiences that required buyers to know exactly what they wanted. This legacy approach still lingers, leaving B2B sales team dependent on outdated systems that hamper efficiency and prolong sales cycles.
Large B2B companies initially embraced ecommerce platforms to offload the customer service workload. However, these early systems treated ecommerce as a simple extension of internal data sources, pulling from ERP systems with little regard for the customer experience. This resulted in a shopping process that leaves buyers sifting through complex catalogs of specs and attributes.
This legacy of ERP-driven data remains one of the biggest challenges in B2B ecommerce and requires buyers to know precisely what they’re looking for, creating friction for buyers and salespeople.
The solution begins with transforming product information into a powerful customer-facing tool. A well-implemented product discovery system uses data to make products discoverable, configurable, and purchasable. Each product attribute is mapped to real customer needs, allowing the data to actively guide the buyer through configuration options.
By investing in the right systems and platforms to simplify product discovery, B2B companies can empower both buyers and sellers, paving the way for streamlined, efficient sales processes.
Bridging the Gap to Enhanced Buyers’ Journeys
More B2B enterprises use AI to bridge the gap between outdated selling models and current customer expectations. However, before AI can truly enhance B2B ecommerce, data hygiene must become a priority. Comprehensive product descriptions, content, and images don’t cut it. Data must be accurate, complete, consistent, relevant, and up to date, requiring rigorous product data cleansing, structuring, and enrichment processes.
When the data house is in order, integrating AI into the ecommerce ecosystem can enhance the buyer’s journey from start to finish.
