What Is Guided Selling And How Can It Help You Improve CX?

Learn from our blog post what is guided selling and how can technology help you achieve it.

Organizations today strive to achieve the perfect customer experience (CX) for each individual they interact with. These efforts are not concentrated at a departmental level, but rather, are reflected across the entire organization. And obviously, the CX executives bear the most significant load of ensuring smooth, streamlined customer interactions. One less-than-perfect interaction will make you lose a customer. And then another one, as word of mouth and negative reviews can damage brand reputation at an accelerated pace. Fortunately, advancements in technology have come to the aid of organizations in the form of modern sales solutions. AI adoption has exponentially increased across industries, but not everybody considers it an almighty savior to all CX issues. 

Only 41% of CX executives today claim they have an AI strategy. In the era when AI mingles its presence in every aspect of our lives, this ratio is on the insignificant side of the spectrum. IBM identified a series of factors that make CX executives eager to adopt AI within their organizations, improving CX being one of the main reasons. But let’s see below how guided selling and technology can bring their fair contribution.

What Is Guided Selling?

Simply put, guided selling is the process of analyzing current and historical sales trends with the help of customer data and tailoring product recommendations to accelerate conversion rates. According to Gartner, 75% of B2B sales will be managed through AI and ML-driven selling solutions. 

Netflix does a beautiful job of recommending your next favorite series. 80% of the shows watched on the platform are discovered via guided selling or recommendations. And many B2B and B2C businesses aspire to reach the same level of “magic.” Guided selling is far from magic and closer to AI-powered data engineering, custom software development, and deployment.

Guided selling is far from magic and closer to AI-powered data engineering, custom software development, and deployment.

Guided Selling in B2B Sales

In B2B, buyer journeys are more nuanced than in B2C or eCommerce. Buyers are more prone to revisit past steps in their buyer journey to ensure they make the right decision. By doing so, they break the traditional process we encounter in the B2C dynamic. For this reason, in B2B, guided selling introduces B2B software selling solutions that aid sales teams in increasing sales by recommending the best content, communication tactics, actions, and products to close a deal.

In this case, companies need powerful AI tools to transform raw data into actionable insights. ML also plays a role here. Tools with this technology behind them can guide sales reps by learning behavioral trends and suggesting the next-best actions based on precise data, historical trends, market conditions, and demographics.

Guided Selling in B2C or eCommerce Sales

In B2C and eCommerce sales, guided sales are performed through recommender tools, mainly. This technology filters and displays product recommendations based on customer interactions and data: think comments, ratings, page views, cart events, click-throughs, and so on. And once again, Amazon was the first to deploy a similar engine more than a decade ago. The rest is history–a history powered by data, customer interactions, and complex, agile algorithms. Over the past decade, the data volumes generated by users have increased the opportunities of providing superior user experiences, helping businesses deliver excellent omnichannel customer experience. Correctly deployed, similar tools can help enterprises increase customer retention, build a loyal customer base, and increase revenue. AI-powered guided selling software can help facilitate CX strategy on different levels.

AI-powered guided selling software can help facilitate CX strategy on different levels.

How Does Guided Selling Work?

How can you leverage AI-powered guided selling to boost company-wide performance KPIs? These systems use different rules and models that can shift a buyer’s purchase decision. Here are some examples that are relevant in B2C and eCommerce dynamics:

  • Last Purchase Model – this model uses a simple principle: what was this buyer’s previous purchase might also be their next. Product catalogs sometimes change, and previously purchased items may no longer be available. Last purchase recommender tool models can retrieve information from user interactions and recommend similar items in a shop’s catalog.
  • Popularity Model – popularity-based models work by ranking products in catalogs by their popularity in a specific timeframe and making suggestions based on this data. In eCommerce, these models take the stage.
  • Association Model – is the user buying a box of chocolates for Valentine’s? A bottle of wine would complement it just right. This is another simple yet effective way recommender systems can help your business increase performance KPIs and improve customer experience in the long run.
  • Time Series Model – this model works on the same principle as the association rule, but it makes it a bit more specific. It uses sequential-based algorithms and registers both long and short-term user preferences.

In B2B, the process is more complex. Sales automation tools are the core of guided selling. AI-enabled sales automation and CRM tools help sales reps uncover unique insights despite the lack of complete data. High-performing software gathers data from outside sources and complies with internal records for a 360-degree view of each customer.

When Should You Consider Guided Selling?

Both B2B and B2C businesses can benefit from guided selling techniques. Dedicated tools that facilitate these process come with a series of benefits:

  • AI-generated insights help your company uncover hidden market opportunities.
  • Increase sales productivity by knowing which leads in your pipeline you should prioritize.
  • Increase customer satisfaction levels by helping your sales reps make the right decisions and recommendations, based on each account’s specifics.
  • Improve customer engagement strategies through predictive case routing and contextual data in real-time.
  • Engage with customers strategically by identifying which customers in your portfolio are most likely to churn.

Guided Selling Best Practices

Despite what many think, the recommended products in guided selling are far from a set of identical items. And this is where many businesses miss their opportunity to tailor unique experiences for their customers and let them become brand advocates (this is how upselling opportunities arise, too). Here’s our take on how you can ensure you follow the safest, cleanest path to guided selling-backed CX.

CX execs have yet to claim to understand their customers in a lack of data.

Understand Your Customers

CX execs have yet to claim to understand their customers in a lack of data. Data is the key to success across all departments within an organization, but sometimes, data needs to be more cohesive, complete, and less redundant.

To fully understand customer behaviors and successfully leverage product recommendations and guided selling, you must ensure that complete datasets drive these recommendations. Although you may have a well-defined customer persona, not everybody in that pool fits the same description and has the exact needs – or budget. These criteria influence their buying decisions, and your suggestions must fold on them. 

For instance, if you own an online clothing store, some shoppers might target dresses, let’s say, and will only shop for products in this category. But not all dresses fit the “perfect dress” description. Some make decisions based on length, others on color or fabric. Others will just religiously buy dresses from a single brand. Efficient product recommendation engines consider all these variables and recognize each visitor as unique. For this, the engine must discover the “why” behind each past purchase decision. This theory is transferable in B2B scenarios as well. In the presence of complete data, your engine will determine what attracts customers and the trigger behind their buying decisions. A successful engine will do more than that. It will also persuade them to increase the average order value with relevant product recommendations. 

There’s Magic in Numbers – And Less Is More 

So, what’s the perfect number of product recommendations? Sometimes, e-stores, B2C, and B2B businesses miss the point by seeing these engines as a means to accelerate conversions. This is why their suggestions are vast. But, in reality, product recommendations exist to enhance customer experience and provide visitors with an enjoyable and relaxing interaction.

While featuring many product recommendations is tempting, this can quickly become a source of decision fatigue, hence frustration for shoppers. Remember, there’s a fine line between guiding visitors and transforming your e-store into a 2000-era website. With that in mind, we recommend quality over quantity when taking this approach. A short list of curated products relevant to your customer and their current shopping sessions will suffice.

Curate a High-Quality Image Gallery for Your Products 

eCommerce businesses have two prerequisites: the products themselves and compelling imagery. In the case of online shops, visual content is fundamental and is a vast component of a streamlined customer experience. High quality visual content can offer your customers a real-life online shopping experience while ensuring they get a product closely matching their needs. Take a look at Zalando. Their recommendations include sharp images of the featured products, increasing their appeal and contributing to a smoother experience for the customer.

Deploy Highly Accurate Recommender Engines

A poorly curated customer experience will probably drive visitors toward your competition – 48% of the respondents claim so, according to Marketing Dive. When investing in recommender engines, you must ensure that the system is developed in a flexible, intuitive, and highly accurate fashion that can predict your customers’ preferences based on multiple data entry points. Engines created using STAMP models are session-based neural network engines that aren’t very computationally complex but highly accurate. Both Zalando and Home Depot successfully implement such models. 

Social Proof and Trust Badges

Customer experience is transferable. Other customers’ trust in your brand might be just the missing piece in tailoring a unique experience. Embedding social proof and trust badges into your recommendations can increase credibility. Adding the number of reviews and the overall rating next to recommended products will boost buyers’ trust in your brand and the products. It will also increase the Average Order Value (AOV).

Contact SugarCRM for Help In Your Guided Selling Processes

SugarCRM can help you work smarter not harder to achieve your CX and selling goals. Our systems can generate accurate predictions to make better decisions in your guided selling process. With the help of AI-powered predictability, your company can get dependable next step advice and gain better visibility through external data.

You can request a demo and see how SugarCRM can help your guided selling process here.

Mihaela Chiurtu
Mihaela Chiurtu As a Marketing Content Writer, Mihaela is passionate about branding, content strategies, and customer interactions. When outside the office, Mihaela is a Netflix binge-watcher, skincare geek, and music lover.

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