Most of us have experienced the feeling of being overwhelmed when confronted with dozens of product choices. (Does standing and staring aimlessly in the cereal aisle sound familiar?) “Choice paralysis” is the term that behavioral scientists use for this universally recognized phenomenon that introduces friction into the sales process, even causing some buyers to abandon product selection altogether.
Here’s where guided selling comes in. These solutions prompt buyers to articulate their needs and preferences early in the sales cycle so they end up with a curated product selection that matches their desires. For businesses that sell configurable solutions, guided selling is a particularly effective tool for overcoming the challenge of packaging and pricing complicated combinations. This article will explore strategic ways that businesses can use guided selling to tap into buyer behavior and build enduring value across a range of retail and business-to-business product categories.
What Is Guided Selling?
Guided selling is a sales methodology that combines sales processes with technology to create customized buyer experiences. Customer data is used to fuel highly personalized interactions that help buyers make better purchase decisions, faster and more efficiently.
Both business-to-consumer (B2C) and business-to-business (B2B) sales environments can benefit from guided selling solutions. Ecommerce websites and applications use a variety of tools, such as interactive quizzes, room visualizers and advanced product filtering, to enable shoppers to find their perfect products. Robust guided selling implementations like these take the guesswork out of self-serve transactions, improving customer experience and increasing conversions.
Similar tools can be used as effectively in B2B self-serve transactions, but for complex or technical solutions purchases, guided selling often works in tandem with a human seller. As sellers uncover buyer information, such as industry, feature requirements and budget, guided selling systems draw upon historic customer data to feed timely and relevant information to the sales rep, such as use cases, product specifications and configuration options. In this instance, guided selling is part of an overall sales-enablement strategy — and businesses seem to be catching on. According to Gartner, 75% of B2B sales organizations plan to augment their traditional sales methods with guided selling solutions by 2025 to better connect with customers and spur conversion.
Artificial intelligence (AI) and machine learning (ML) are key drivers of this trend. ML tools analyze data from customer demographics, digital interactions and customer support inquiries, using this information to expose correlations and generate insights that inform the sales process. As buyer-seller interactions progress, generative AI may be used to influence seller actions, curate relevant content assets and draft responses in real time.
Key Takeaways
- Guided selling solutions benefit both B2C and B2B transactions by creating personalized experiences that increase customer confidence and satisfaction.
- Sales organizations can use guided selling to equip sales reps with selling systems that promote sales-process consistency and accelerate deal closures.
- Artificial intelligence is fueling guided selling solutions, tapping into customer data insights to optimize sales performance.
- Guided selling is often paired with pricing and configuration tools to streamline customer proposals and reduce errors.
- Integrating guided selling with existing software systems that house business and sales-related data further optimizes the sales-to-delivery workflow.
Guided Selling Explained
Guided selling is a sales technique or process that aims to assist and direct customers through their buying journey. By leveraging information specific to the individual, guided selling funnels buyers toward products that will satisfy their unique needs.
If guided selling is an engine, then data is its fuel. Real-time data informs the buyer’s path, triggering guidance, recommendations and support tailored to that specific purchase decision. Context-rich data is gleaned directly from the prospect, via a variety of inputs. In an ecommerce environment, quizzes, side-by-side image choices and chatbots can capture buyer preferences; for a complex B2B solutions sale, initial customer profiling is achieved using marketing automation tools and sales conversations.
This explicit personalization differentiates guided selling from decision-support tools that use aggregate buyer data alone to recommend products. (Think people who purchased this also purchased these prompts.) Guided selling can certainly incorporate aggregate buyer data to help inform recommendations to customers, but its scope is much broader. In guided selling, all steps of the buyer’s journey incorporate interactivity and decision support, not just product specification. This means that detailed criteria, such as specific use cases, demographics and firmographics, and budget, may influence the decision support that is presented to the buyer, as well as steer custom product configurations.
Benefits of Guided Selling
Guided selling facilitates personalized buyer experiences, which are associated with higher conversion rates and better revenue outcomes for the businesses that activate them. Benefiting both buyer and seller, the methodology delivers several powerful advantages:
- Improved customer experience: Customers have come to expect personalization, according to McKinsey and Co. Its research cites ease of navigation, tailored messaging and relevant product recommendations as factors that most influence purchase behavior and positive brand association. The personalized buyer experiences that are facilitated by guided selling save customers time, which drives loyalty and, ultimately, increases their lifetime value.
- Increased buyer confidence: According to Gartner, customers spend two-thirds of the B2B buying journey gathering and comparing information. While the information is perceived to be high quality and trustworthy, buyers say they experience information overload and contradiction among suppliers. Helping buyers make sense of the information — beyond simply supplying it — is a key to unlocking customer confidence. Buyers must feel reassured that they are asking the right questions and correctly prioritizing the information that matters most to their unique requirements. Guided selling helps B2B sales reps build customer confidence by purposefully prompting the reps to share information selectively, matching a buyer’s persona, industry and other attributes at every stage of the purchase decision. The point is to use insights gleaned from observing hundreds or thousands of buyers implementing similar solutions to educate potential buyers with acutely relevant, context-rich information. This raises seller credibility, saves time and supports customer success over the long term.
- Enriched customer data: Supporting buyer-specific journeys produces a lot of data, including responses generated via questionnaires, chats and live conversations, as well as behavioral data, like content consumption. Data mining tools are often used to capture and analyze this data. Using a guided selling approach, business leaders can then apply these data-driven insights to increase the relevance of buyer communications, modify product offerings and improve the performance of sales activities. For example, discovering that buyers who downloaded a particular knowledge asset were more likely to have higher customer satisfaction scores could inspire a more deliberate use of that asset during the sales process. Or recognizing a pattern in product filtering could spark new product configuration options.
- Optimized cross-selling and upselling: Offering relevant recommendations during the sales process is a strategic way to increase average order value. Guided selling uses personalization to sharpen the relevance of complementary product recommendations, increasing conversions and delivering a better customer experience.
- Improved sales organization effectiveness: Imagine the impact that a material improvement in new rep onboarding time could have on sales performance. With the whole team using the same guided sales playbook to coach buyers to make informed purchasing decisions, customer communication stays on-message and relevant. Moreover, costly mistakes in product configurations and price quotes are avoided.
- Strengthened omni-channel strategy: With B2B online transactions growing steadily, businesses can make even complex purchases easier with guided selling tools that facilitate self-guided journeys. Guided selling promotes a consistent customer experience by making captured buyer information and preferences accessible across multiple channels, such as online research followed by a “last mile” sales-assisted transaction. If a company’s ecommerce strategy is just starting to take shape, embedding guided selling at the outset can accelerate adoption among buyers and sales team alike.
How Guided Selling Works
To paraphrase Albert Einstein, guided selling should be as simple as possible, but no simpler. In practice, guided selling solutions can be complex, because anticipating and responding to unique buyer inputs in real time with authentically relevant information demands both process rigor and sophisticated technology.
Guided selling depends on three data-related activities: gather, interpret and present. From the buyer’s perspective, the data gathering occurs at the start of the buyer’s journey via form-fills, questionnaires and/or conversations with sales reps. If the buyer is a returning customer, data from their record in the seller’s customer relationship management (CRM) system augments their profile, strengthening the inputs that will drive personalized recommendations.
From there, a guided selling solution generates product or solution options that meet the buyer’s criteria. To enable that, AI or ML algorithms interpret buyer requirements using rules-based logic. This logic might be limited to basic product attributes — a fertilizer company may be able to produce a tailored recommendation based only on inputs like coverage area and crop type — or it could expand to incorporate a breadth of related data. For example, ML algorithms can draw upon scores of similar customer profiles, use cases and preferences to inform recommendations. Rules-based algorithms ensure that recommendations match available configurations and/or trigger custom configuration options.
Along with product recommendations, buyers are commonly supported with relevant information that deepens their understanding of the “why” behind the recommendation. A key objective of guided selling is to strengthen buyer confidence, which facilitates brand trust and loyalty. AI tools are useful at this step, curating relevant information and generating text for customer communications.
Once a buyer makes a selection, a proposal or quote is generated. Here, a configure, price, quote (CPQ) solution prepares the recommended products, configurations and pricing that will be presented to the customer. This is another key systems integration point, with the CPQ solution tapping into databases that house inventory, shipping and tax fees, relevant product add-ons and special offers.
Examples of Guided Selling
Once a business becomes familiar with the fundamentals of guided selling, it’s useful to consider examples that fall within three common use cases.
- Product finder: Buyer requirements and preferences create a “recipe” for recommendation engines to follow to filter product choices. A website selling tablecloths might pose a series of simple questions to site visitors to reveal size, material and style preferences. From there, a buyer would be presented with thumbnail images of products that meet only those preferences. In B2B environments, the sellers often operate the filters, drilling into product taxonomies to accelerate conversations with buyers.
- Product configuration: Often, buyers don’t have the experience to rule out products that are incompatible or inappropriate. Simply put, they don’t know what they don’t know. Guided selling protects buyers and sellers from making such mistakes with rules-based systems that suppress mismatched features or combinations. For example, an industrial cleaning solutions company can use a combination of buyer-supplied information (e.g., what needs cleaning) and its own data (e.g., a list of chemicals that are prohibited in the buyer’s state) to present the buyer with suitable options. To achieve this, guided selling solutions rely on CPQ tools to configure solutions and calculate a price proposal or quote.
- Focused decision data: Content that helps buyers navigate complex decisions is a powerful asset to leverage in guided selling. An industrial automation company might have a comprehensive library of reference materials that demonstrate the business benefit of automation, explain product considerations and prepare manufacturers for implementation. Guided selling takes that a step further by ensuring that only the most relevant resources are shared, reducing information overload and positioning the sales rep as a strategic adviser.
See how NetSuite CPQ can save your staff time…
Guided Selling Best Practices
Once a business is ready to implement guided selling, the following best practices can help support a successful implementation:
- Start with clean data. Since data is the anchor to guided selling, it’s critical that customer data and product data are accessible, accurate and updated. In this context, customer data includes CRM records, as well as the trove of text data generated from customer service inquiries, chatbots, online reviews and more. Product data includes the SKU numbers, descriptions and compatible configuration options offered. Ensuring that all product references are consistent (e.g., 10 inches, not 10” or 10 in.) is a prerequisite for accurate recommendations. Data expertise is required to consolidate and analyze the data, mining it for patterns that produce the insights that will propel the sequences underpinning the guided selling strategy. This type of data discovery has evolved dramatically with the availability of AI tools that automate parts of the effort, including collection, analysis and visual presentation. These advancements help businesses of all sizes take advantage of data analysis.
- Use a documented sales process. This is particularly relevant for businesses implementing guided selling implementations for sales-assisted buyer’s journeys. Sales leadership should be involved to help construct “next-best action” rules based on existing sales methodology, often referred to as a sales playbook. Implementation teams should work collaboratively with leadership to determine which repetitive tasks could be better handled by AI-enabled tools. Incorporating existing sales behaviors into the guided selling solution can minimize adoption hurdles, as sellers adapt to using the system.
- Walk before running. A guided selling solution should start with the basics and evolve and expand over time. For example, getting a CPQ capability down pat can be a springboard to increased sales efficiency and improved buyer experience.
- Provide choice. Depending on the industry, businesses should aim to recommend several products or solution choices. The goal of guided selling is to make the buying decision easier, not dictate it. Increasing buyer confidence means equipping buyers with contextual information that helps them prioritize their requirements and explore the implications of product choice. This might come in the form of explainer videos, case studies, user reviews and/or tip sheets.
- Plan ahead. An effective guided selling implementation takes proactive steps to address points of friction — or dead ends — that could result in abandoned buyer journeys. For starters, businesses should ensure that product suggestions are limited to appropriate and configurable solutions — no one should end up with drop handlebars on a mountain bike. For items that meet the buyer’s criteria but are not in current inventory, sales organizations should establish business rules for the next action. For example, proposals can include order availability dates or suitable alternatives to out-of-stock products.
- Integrate guided selling with business-process tools. Real-time data, such as previous customer interactions and product availability pulled from CRM, ecommerce and enterprise resource planning (ERP) systems, supports sellers with relevant information in the moment. Purchase decisions made within the guided selling system can connect to inventory and fulfillment platforms, eliminating separate data entry. And all of the new data generated from guided selling interactions can funnel back into the system’s data set to propel ongoing improvements. Collectively, this approach is a force multiplier on deal acceleration and customer experience.
Drive Efficiency and Accelerate Sales With NetSuite
NetSuite CPQ solution offers guided selling capability, providing an ecommerce-like catalog experience and powerful filtering tools for thousands of SKUs. NetSuite CPQ works seamlessly with NetSuite ERP, CRM and ecommerce solutions to enable sales teams to configure, price and quote complex products with accuracy and reliability. Connected workflows automate the sales-to-delivery process, seamlessly generating bills of materials and routing instructions. The integrated solution enables businesses to leverage existing customer data and business processes to implement a customized guided selling strategy.
Customer satisfaction starts with the right product selection, but the proliferation of product options and configurations can stymie even the most diligent buyer. Guided selling sets up businesses and their customers for success with a tailored sales process that uses buyer data and subject matter insights to craft perfect-fit solutions. Businesses that deploy guided selling solutions are positioned for faster deal cycles and increased customer satisfaction, as well as reduced exposure to costly item returns.
#1 Cloud ERP
Software
Guided Selling FAQs
What is the guided selling process?
The guided selling process is a series of repeatable steps that help buyers select products and configurations that meet their unique requirements. Sales organizations often use playbooks to codify the guided selling process.
What is guided selling in ecommerce?
Ecommerce websites use tools that include interactive quizzes, sliders, filter panels and chatbots to present a curated selection of products that align with the buyer’s inputs. Often, text, image and video assets play a role in explaining the decision factors or illustrating technical product details.
What technology is required for guided selling?
Guided selling depends on three activities: gather, interpret and present. To gather the type of customer data that drives product recommendations, businesses should consider interactive tools, such as questionnaires and chatbots, in addition to tapping into an existing customer relationship management (CRM) system. Interpreting this customer data to aid product selection will rely on a guided selling tool that can handle rules-based algorithms. Often, these tools work as part of, or in tandem with, a configure, price, quote (CPQ) solution, which prepares the recommended products, configurations and pricing that will be presented to the customer.
How are guided selling systems different from static recommender systems?
In short, static recommender systems are entirely automated and use algorithms exclusively to filter or prioritize offerings. Netflix predicts which movie you’ll enjoy with this kind of system. By contrast, a guided selling solution leads a buyer through a step-by-step process to collect information and uses these interactions to help the seller select product options and present contextual information to support the customer’s buying decision.
How does AI work in guided selling?
Artificial intelligence (AI) is integral to guided selling implementations and is used to direct purchase decisions in real time. AI-driven conversational intelligence, where buyer conversations are analyzed in real time, can complement AI models that profile users based on historical data. The speed and accuracy of the resulting product recommendations are enabled by AI’s ability to make associations across multiple datasets.
