Why Retail Industry Needs a Specialized CX Automation Solution

In today's fast-paced world, customers demand quick and personalized experiences from the retailers they shop from.

Customer experience (CX) has become a vital aspect of any retail business. CX automation solutions, including chatbot solutions, can help retailers offer a seamless and consistent experience across all channels, which is critical to retaining customers and gaining new ones.

Despite being early adopters for CX automation tools, the retail industry has had many challenges in getting significant results when simply adopting horizontal CX automation solutions. 

In this article, we dive into the following topics:

  1. What makes the retail industry an especially complex one for CX automation solutions.
  2. What CX automation done right looks like.
  3. A next-gen vertically specialized CX Automation solution.

Retail Needs CX Automation

Retail is an industry that is under high pressure both in terms of customer expectations and sensitivity to high costs. Automation and self-service are the only path to delighting customers while keeping the cost low.

According to Business Insider, retail is the #1 industry utilizing chatbots, a key form factor of CX automation. 72% of total chatbots accessed are retail chatbots. 

Consumers seem to like it too. Invesp found that 34% of consumers prefer customer service chatbots in eCommerce rather than other service-oriented places such as banks. 

So, what’s holding back the retail industry from leveraging automation to handle most of their customer service?

It turns out that the challenge is not in the demand from either the consumer side or the business side, the challenges are in the technology. Early generation chatbot solutions are challenged with rigid user experience, poor resolution capabilities, and high implementation costs.

In the next section of this article, we dive into the complexities that make Retail CX Automation uniquely challenging. 

Complexities For Retail CX Automation

CX Automation complexities vary from industry to industry, and are primarily tied to the nature of its core use cases. There are three use case categories, and each posts different automation requirements and complexity:

Knowledge application use cases

These are use cases where the desired “resolutions” are generic informational answers. Examples of such use cases include “return policy questions”, “how does the loyalty program work”, “where are your headquarters”, etc. 

Business-controlled engagement flows

These are use cases where businesses, led by their own objectives, want to set up specific flows and tightly control both the flow logic and the content presented by the flows. Such use cases typically include promotional use cases. For example, engaging the customer with a survey with a specific flow or promoting to customers the latest sales events after a specific type of interaction. 

Workflow application use cases 

These are the use cases that represent the bulk of transactional issues, where the resolution requires running a business-specific workflow, tapping into the user’s specific data, and often involve taking some action.

Examples of such use cases include simple workflow services such as looking up a nearby store to complex workflow services such as making a product exchange or resolving a delivery issue complaint. 

From the automation requirement perspective, the three categories of use cases require different AI technology, different solutions, and different tools.

Early generations of CX automation solutions focused on tackling the first two categories of use cases.

They either offered little resolution power to the 3rd use case category or required extensive custom data integrations and custom crafted dialog flows to tackle the 3rd use case category. 

However, in the retail industry, the workflow application use case category is typically the dominant driver for contact centers.

Specifically, a typical retail or eCommerce brand sees over 85% of their contact center inquiries being in the category 3 use cases (ie Workflow application use cases).

The chart below shows the aggregated customer service inquiries distribution across Linc’s client base. 

Figure 1. Retail CX Automation Needs by Use Case Categories. (Data based on actual customer inquiries aggregated across Linc client base). 

The combination of a large percentage of inquiries requiring workflow resolutions and the number of distinct use cases that make up these inquiries makes Retail CX Automation especially challenging. 

In the remainder of this article we dive into the technology and solution requirements for each of these three categories of use cases, what’s out there in the market, recent technology advancements, and ultimately, answer the question – “Why does the retail industry need a specialized CX Automation solution”.

Technology Requirements for Retail CX Automation Done Right

Retail CX Automation done right requires bringing together technologies, solutions and tools that are tailored to address each of the three categories of use cases into one synchronous solution.

Forcing the wrong CX tech can lead to poor user experience and undesirable business outcomes. 

Specifically, technologies for knowledge application use cases have been long available. These include a combination of technologies that can work together to manage, organize, and present information effectively.

Typically, taxonomy and categorization are used to organize the content of the knowledge base into logical categories and structures, and search functionality is essential for making the content of the knowledge base accessible and usable to end users.

Lexical search (keyword based) and semantic search (semantic meaning based) are frequently used in knowledge applications. The advantage of such technologies is that it’s generic and independent of the knowledge base itself.

As long as there is a high quality knowledge base available, it can be implemented quickly with little additional integration requirements other than access to the knowledge base source data. 

The recent advancement in deep-learning based large language models, such as OpenAI’s GPT  models, leapfrogs conventional knowledge base solutions with the ability to “generate” human-like text and understand the nuances of natural language.

Unlike conventional knowledge base solutions, which rely on curated databases of structured information, GPT  can generate new insights and understandings from unstructured data.

However, these large language models offer limited control over output and limited interpretability, which can be highly critical for certain CX automation use cases. 

Figure 2. Comparing knowledge use case automation AI solutions - Generative large language model-based solutions vs. Conventional Knowledge-base Solutions.

Neither conventional knowledge-base solutions nor large language model-based solutions are designed for workflow applications. 

Conventionally, workflow applications are automated with curated dialog-flows. In fact, most chatbot solutions in the market fall into the category of dialog-flow solutions.

Figure 3. Example of conventional 'dialogue flow' scripted AI chatbot

Some of these solutions offer a no code UI to improve the experience of the dialog flow building process.

AI is also utilized by most of the dialog flow solutions, but the use of AI is limited to “intent understanding”, not “resolution”.

These dialog flow solutions are best suited for “brand-controlled engagement flows”, and when they are used to address workflow use case automations, businesses often are challenged with poor results and high implementation overhead.  

Linc’s patented “no dialog flow” technology takes conversational AI for workflow applications to a whole new level.

Figure 4. Linc AI technology makes it possible to register any workflow-oriented service to a conversational AI OS through a simple declarative language.

Instead of curated dialog flows that may only capture limited “happy paths”, Linc’s “no dialog flow” technology can generate versatile conversations that are optimized for the most effective workflow “resolution”. 

The Next-Gen Retail CX Automation Solution

Linc’s next-gen CX automation solution brings together all three pillars of technology requirements for a synchronous solution.

Figure 5. Linc next-gen CX Automation solution brings together all 3 pillars of AI technology for best automated resolution rate and user experience.

Beyond AI, Linc’s next-gen CX automation solution also dramatically reduces the tech stack / integration requirements, and model training requirements by taking advantage of the retail industry’s common data feeds and pre-built integrations.

The following purpose-built for retail solution components dramatically reduce implementation efforts while improving results. These are just as critical to the CX automation overall performance as the AI pillars:

Purpose-built data pipelines:

  • Standardizing essential data feeds ingestion.
  • Streamlining how data is utilized for AI training, maximizing training quality while reducing training effort.
  • Automatically mastering business domain knowledge (such as product knowledge) by utilizing available data feeds, reducing manual training.
  • Automatically construct context that can be used to personalize the experience and maximize resolution. 
  • Such data pipelines include product catalog data, order data, shipment data, promo code data pipelines. 

Configurable on-platform service engines:

  • Bringing industry essential service engines on to the platform to reduce client integration or tech stack requirements. 
  • Offering “ready” digital workforce with proven resolution capabilities with little custom integration
  • Such essential service engines include order support engines (from the status of an order to return / exchanges, to delivery issue support), product support engines, inventory support engines, and store support engines. 

Vertically specialized knowledge graph:

  • Making the AI “speak retail” from day 1. 
  • Allow for advanced “inference” to be handled at platform level without business-specific training overhead. 
  • Complement the large language models with industry specific domain knowledge.
  • Allow for advanced analytics and reporting. 

In Conclusion

The unique complexities of the retail industry make it both necessary and rewarding for an industry specialized CX automation solution.

Retail CX Automation done right requires a synchronous solution of multiple AI pillars, as well as a purpose-built solution stack.

Next-gen vertically specialized solutions, like Linc’s Retail CX Automation platform, take it to a whole new level across automation resolution performance, interaction experience, and ease of integration.

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