Tag Archive for: data

Transforming Mystery Shopping with AI: How HS Brands is Revolutionizing Customer Experience Measurement

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HS Brands, a leader in mystery shopping services, demonstrates how AI technology can create compelling competitive advantages.  The approach uses AI to reshape how HS Brands gathers, analyzes and acts on the data supporting customer experience measurement.

Integrating Large Language Models (LLMs) into enterprise workflows has become a crucial strategy for enterprises to enhance efficiency, consistency and quality. A common LLM framework is Retrieval-Augmented Generation (RAG) which focuses on information retrieval and synthesis. However, LLMs offer a broader spectrum of capabilities that can be embedded into otherwise manual business processes to drive significant impact.This experience shines the light on other opportunities to apply AI to traditional business processes while creating strategic advantages throughout an organization.

Industry Background

As businesses prioritize customer-centric strategies, the demand for mystery shopping services has surged, creating opportunities for firms like HS Brands. Many customer-facing industries rely on mystery shoppers to report on their experience when interacting with the business.

These reports often contain a combination of multiple-choice question responses and detailed narratives. HS Brands use editors to ensure that raw shopper reports are complete and consistent, to provide clients with reliable insights. Editors identify discrepancies between dropdown answers and narrative descriptions, then have the shopper reconcile or elaborate in their own voice. Reviewing a single shopper’s complex report is a meticulous task that can consume hours. Integrating AI using LLMs into this process offers a transformative solution to improve efficiency and accuracy.


Challenge: Highly manual review processes

The manual review of mystery shopping reports requires editors to carefully cross-reference dropdown selections with extensive shopper-written narratives. This labor-intensive task is time-consuming, leading to potential delays in report delivery and increased operational costs. In an industry where timely and accurate feedback is essential for client decision-making, these inefficiencies can result in lost opportunities and diminished client satisfaction.

The transformation is particularly evident in the editing process. “Automating many of the editors’ time-consuming tasks, allows them to refocus their brain power towards identifying survey insights,” explains Tommy Mills, CEO of HS Brands. Automating many of editor’s time-consuming tasks, allows them to refocus their brain power towards identifying survey insights

Approach: Use AI to automate time consuming steps in the workflow

Ironside works with clients to identify opportunities for AI in their business and implement in a low risk process. By consulting with business domain subject matter experts and prioritizing AI use cases by rubrics evaluating suitability and impact, the high value uses of AI specific to a client are uncovered. 

The generative AI solution that emerged from Ironside’s process, leverages LLMs to automate the detection of inconsistencies within mystery shopping reports.  This greatly streamlines the editor workflow. Prompted appropriately, the LLM identifies missing responses and possible contradictions between dropdown responses and narrative content, flagging these sections for follow-up by human review.

The prompt’s instructions to the LLM include: 

  • Report reviewing guidelines
  • Sample editor comments
  • Step by step instructions

This custom engineered prompt helps the LLM understand the context and typical patterns of shopper report inconsistencies.  The LLM and core AI functionality is implemented within an Amazon Q Business web app that can process new reports in seconds, rapidly flagging any discrepancies for further human review.  The output from the Amazon Q Business app gives the editors a flying start to thoroughly understand the number and nature of reconciliations needed for the report.  Editors can adapt the editing guidance to help individual shoppers learn how to deliver more consistent and accurate reports. 

Strategic advantages of AI implementation

There are 3 key operational benefits in adopting generative AI:

  1. Enhanced Data Quality – “The editors can focus on more than highly intelligent language, they can drill in on the details, asking shoppers for more detail, and ultimately giving the client a better product,” notes Mills. This improvement in quality creates a compelling differentiation in the market.
  2. Consistent Evaluation Standards – AI provides standardized analysis across different regions and markets, addressing a critical challenge in maintaining quality across diverse locations. The technology ensures that evaluation criteria are applied uniformly, regardless of geography or editor.
  3. Operational Efficiency – The automation of routine tasks leads to significant time savings. As Mills points out, “AI would save a good amount of time…” and indeed early estimates suggest a 25% reduction in the editing cycle.

Creating strategic value for clients

AI implementation delivers more than operational benefits. Through AI, HS Brands offers insights beyond standard mystery shopping offerings.  One example area is Training and Development.  An AI system can identify patterns in customer service delivery and provide actionable insights. “Mystery shopping data does feed training insights,” Mills emphasizes. “Our recommendations can range from micro issues like ‘Team Member X made this mistake at location 5’ all the way to ‘We’ve noticed in Region 7 that we’ve got a much bigger problem.'”

Expanded mystery shopper offerings powered by AI include:

  • Deeper Analytics –
    • Pattern recognition across locations and regions
    • Trend identification in service delivery
    • Predictive insights for training needs
  • Enhanced Training Support – The AI system can:
    • Identify specific training needs by region
    • Create heat maps of performance issues
    • Generate targeted recommendations for improvement
  • Strategic Decision Support – The system provides different insights based on organizational level:
    • Location managers receive specific operational insights
    • District managers get trend analysis
    • Regional managers access strategic patterns
    • Corporate leadership obtains system-wide insights

“Clients win because we can give expanded mystery shopper offerings and deeper insights using AI”, Mills states, highlighting the potential for differentiation in a highly competitive space.

Conclusion: When considering LLMs, think beyond RAG – think workflows

Enterprises can embed LLMs in business workflows to improve efficiency, enable higher quality work, and boost customer satisfaction. AI assistants can leverage frameworks other than RAG, and it’s important to consider the right approach to building an LLM solution. In this use case, by automating preliminary review and implementing consistency checks by the AI, report reviewers are enabled to spend more time focusing on quality and educating shoppers on better reporting. These advantages become differentiators in the market and improve customer satisfaction, proving the value of AI solutions.

For organizations looking to leverage AI in their customer experience programs, the time to act is now. The competitive advantage gained through early adoption of these technologies can create significant market differentiation and deliver lasting value to both the organization and its clients.

Ready to transform your customer experience measurement program with AI? Contact Ironside to learn how we can help you achieve operational excellence and deliver enhanced value to your client: GetInsights@IronsideGroup.com

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Why outsourcing can make all the difference when it comes to successful data and analytics transformation

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What causes advanced analytics and AI initiatives to fail? Some of the main reasons include not having the right compute infrastructure, not having a foundation of trusted data, choosing the wrong solution or technology for the task at hand and lacking staff with the right skill sets. Many organizations deploy minimum valuable products (MVP) but fail to successfully scale them across their business. The solution? Outsourcing elements of analytics and AI strategy in order to ensure success and gain true value.

64% of leaders surveyed said they lacked the in-house capabilities to support data-and-analytics initiatives. 

It’s essential to implement a data-driven culture across your organization if you’re looking to adopt advanced analytics. One of the keys to a data-driven culture is having staff with the correct skills that align with your initiatives. In our study, 64 out of 100 leaders identified a lack of staff with the right skills as a barrier to adopting advanced analytics within their organization. Even for organizations that do have the correct skill sets, retaining that talent is also a barrier they face. This is where outsourcing comes in.

Borrowing the right talent for only as long as you need it can be an efficient path forward.

Outsourcing parts of your analytics journey means you’re going directly to the experts in the field. Instead of spending time and money searching for the right person both technically and culturally, outsourcing allows you to “borrow” that talent. The company you choose to outsource to has already vetted their employees and done the heavy lifting for you. With outsourcing, you can trust that your organization is working with professionals with the skill sets you need.

Aside from securing professionals with the correct skill sets, there are plenty of other benefits to outsourcing your organization’s analytics needs. Professionals with the skill sets necessary for advanced analytics and AI initiatives can be very expensive. Outsourcing provides a cost-effective option to achieve the same goal. Rather than paying the full-time salary and benefits of a data science or analytics professional, an organization can test the value of these kinds of ventures on a project to project basis and then evaluate the need for a long-term investment.

Freeing full-time employees to make the most of their institutional knowledge.

Another benefit of outsourcing analytics is the increased productivity and focus of your organization’s full-time employees. By outsourcing your organization’s analytics, your full-time employees will naturally have more bandwidth to focus on other high priority tasks and initiatives. Rather than spending their time on what the outsourcing company is now working on, the full-time employees can dedicate their time to work on things that may require institutional knowledge or other tasks that are not suited for a third party. It’s a win-win situation for your organization – your analytics needs are being handled and your full-time staff is more focused and still productive.

There are many areas of analytics that an organization can outsource. These areas include but are not limited to viability assessments, prioritization of use cases, managing the ongoing monitoring, performance, maintenance and governance of a solution and implementing and deploying an MVP or use case.  In the words of Brian Platt, Ironside’s Practice Director of Data Science, “A partner with advanced analytics and data science capabilities can rapidly address AI challenges with skills and experience that are hard to develop in-house.”

Mid-tier organizations need the right talent and tools to successfully realize the value of their data and analytics framework in the cloud. The Corinium report shows that many companies are increasingly prepared to work with cloud consulting partners to access the skills and capabilities they require. 

 Areas that mid-market leaders consider outsourcing.

Overall, more and more data leaders are turning to outsourcing to help fill the gaps and expedite their organization’s analytics journey. Outsourcing services can help your organization reach analytics goals in many different areas, not just AI and Advanced Analytics. 

Organizations rely on outsourcing in key areas like these:

  • Developing a data and analytics cloud roadmap
  • Assessing advanced analytics use cases (figure shows 68% would consider outsourcing)
  • Implementation and deployment of a MVP, or use case (figure shows 43% outsource)
  • Developing and maintaining data pipelines
  • Documenting and assessing your BI and overall analytics environment(s)
  • Migrating your reporting environment from one technology to another
  • Overall management and monitoring of analytics or AI platform (figure shows 42% are already outsourcing)

When your company plugs into the right skill sets and processes, there’s nothing between you and a successful data-and-analytics transformation.

Take a look at the full whitepaper to learn more: Data Leadership: Top Cloud Analytics Mistakes – and How to Avoid Them

Contact Ironside Group today to accelerate your Advanced Analytics and AI Strategies.

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A message from Ironside’s CEO: We’re here to help.

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Over the past week, I’ve spoken to a number of customers and partners who are adjusting to the ever-evolving reality of life during COVID-19. Beyond the many ways it has affected their personal lives and families, we’ve also discussed how it has impacted their jobs, and the role of analytics in the success of their organizations.

During these conversations, a few consistent themes have emerged from the people responsible for delivering reporting and analytics to their user communities:

  • Reliability: Continuing to deliver business as usual content despite a suddenly remote workforce
  • Resiliency: Hardening existing systems and processes to ensure continuity and security
  • Efficiency: Delivering maximum value even in the midst of a short-term business downturn
  • Innovation: Finding new ways to leverage data to address emerging challenges in areas such as supply chain, customer service, pricing optimization, marketing, and others.

While none of these topics are new to those of us in analytics, the new reality brought on by COVID-19 has made it even more important for us to succeed in every area. In an excellent Forbes article, Alteryx CEO Dean Stoecker discusses the importance and relevance of analytics professionals in driving success for their organizations in these trying times.

As he correctly concludes,

“If anyone is prepared to tackle the world’s most complex business and societal challenges—in this case, a global pandemic—it is the analytic community.”

We’re all in this together.

At Ironside, we’re taking that challenge to heart and looking at how we, too, can refocus our talents to better help our customers. Our upcoming series, Strategies for Success During Uncertain Times, will cover the steps we’re taking to help our partners weather this storm.

As of today, we’re:

  • Holding on-demand “Coffee Breaks” with some of our most experienced SMEs
  • Increasing remote trainings on key technologies
  • Rolling out short-term hosted platforms to accelerate model development, especially for predictive analytics
  • Expanding our managed-services capabilities for platforms and applications, even for short-term demand
  • Increasing our investment in off-shore capabilities to reduce costs and expand coverage models and other areas, too

Additionally, we are offering more short-term staffing options to our customers. Read Depend on Ironside for your data and analytics bench for short- and long-term success for more about these services.

We’re here to help.

At Ironside, we agree that the analytics community is uniquely-positioned to help our organizations weather the COVID-19 storm, and we’re committed to making our customers and partners as successful as possible.

We look forward to speaking with you about your immediate needs, and continuing the conversation on these and other timely topics.

Contact us today at: Here2Help@IronsideGroup.com

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The Importance of Data Preparation for Business Analytics

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In today’s “Big Data” era, a lot of data, in volume and variety, is being continuously generated across various channels within an enterprise and in the Cloud. To drive exploratory analysis and make accurate predictions, we need to connect, collate, and consume all of this data to make clean, consistent data easily and quickly available to analysts and data scientists.

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How To Start Your MDM Journey on the Right Foot

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Any journey requires a few things before getting started. Wandering through the forest can be a very pleasant experience, but if you don’t plan ahead and bring your compass and map, what happens if you get lost? (I know, you probably brought your smart phone, which has GPS. But then you find there is no signal, way out here in the forest…). Before starting an adventure like this, you need to prepare and make sure you are ready for any obstacles or unknowns that could occur.

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Analytics Anarchy: How To Manage a Multi-Vendor Analytics Ecosystem

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When you think about the different ways that data gets used in your company, what comes to mind?

You surely have some executive dashboards, and some quarterly reports. There might be a reporting portal containing everything that IT created for anyone within the past decade.

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Ironside Tech Tip: Cognos Data Sets – An Alternative Approach to Data-Driven Prompts

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What is the most common, most vital need of any business? Speed.

Speed to adapt, to respond, to evolve. It is important, not just at the big picture level, but on a daily basis as well. If you’re constantly waiting for information, then you’re spending less time analyzing data and making decisions that help the organization. Speed has been vitally important in the acceptance of Mode 2 style content such as Dashboards and Stories. Response time is key when viewing daily high-level metrics or when creating that “single use” asset to analyze a potential issue.

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Snowflake: A Modern Cloud-Based SQL Data Warehouse

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With the maturing and ever increasing acceptance of the cloud across multiple industries and the data gravity gradually moving to the cloud, i.e. more data being generated in the cloud, we are seeing some interesting cloud-based data and analytics platforms offering unique capabilities. Some of these platforms could be disruptive to the established market leaders with their innovative thinking and ground up design that is “born in the cloud and for the cloud.”

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What to Consider When Building Your Data Democratization Strategy

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Data democratization is the ability of an organization to provide information to end users in an easy and effective way. The goal is to provide self-service of information to end users with minimal IT support. There are many things that can go wrong when rolling out data democratization projects. The purpose of this article is to identify potential issues and provide guidance on how to avoid them in the democratization process.

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Having Trouble With Your Data Strategy? Then Map It.

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When asked “What’s your data strategy?” do you reply “We’re getting Hadoop…” or “We just hired a data scientist…” or “If we only had a data lake, all our problems would be solved…”? Plotting a good data strategy requires more than buying a tool, hiring a resource, or adding a component to your architecture. You need something to describe:

  • the goals you are trying to achieve,
  • the stakeholders you are trying to serve, and
  • the internal capabilities required to satisfy those stakeholders and achieve those goals

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