To better operate its business around the needs of its clientele, The Vitamin Shoppe created tactical store operations plans around leveraging accurate and reliable shopper traffic data. Paramount to the plan’s success was not only the accuracy of traffic data, but also the ability of its solution provider to quickly and effectively deploy 731 stores within 2 months.

Originally a direct-to-consumer brand, UNTUCKit faced challenges in obtaining comprehensive data for its physical stores. The data points were anecdotal and based on store managers' experiences, lacking baseline metrics to measure traffic and conversion accurately. This made it difficult to verify traffic and conversion rates reported by store managers, who often counted multiple groups of shoppers as one.

Having experienced rapid growth of its business, Snow Peak recognized its retail operations were highly fragmented, with store managers provided a broad authority over store layouts and operations. Additionally, the organizational culture was not adequately rooted in performance and accountability as there were limited data points -POS data, the number of new customers acquired and a manual tracking of the number of shoppers per day -to measure store performance. In order to keep its attention keenly focused on providing the finest products and services to its discerning clientele, Snow Peak committed to establishing baseline retail operations metrics and creating and empowering an organizational culture based on performance, accountability and continuous improvement.

Consistent traffic, but also consistent lulls in conversion. In-store analytics showed consistent traffic at an international airport location, but revealed consistently low conversion at specific times and days of every week. The retailer was interested in using in-store analytics to better understand what factors were contributing to the consistently under-performing periods of time.

The larger than life brand had previously installed another retail analytics solution which failed to provide a layered and contextual understanding of the in-store experience. This resulted in inaccurate traffic counts, conversion rates, and wasted labor hours due to incorrect store traffic forecasts. Additionally, there was a lack of support post-deployment and a lack of comprehensive data about shopper journeys in stores.

By using existing POS coin slot data, the customer could only tell the number of times a gaming machine was played but lacked visibility on other performance metrics such as; 1. Average number of plays per customer 2. Average time spent on each gaming machine 3. Average transaction value (ATV) per visit, per customer 4. Male vs. Female spending/gaming habits

The retailer was interested in replacing an outdated, legacy solution with more current technology. At stake was the sales support group’s credibility with regard to its internal traffic and conversion measurement initiative. In fact, the success of the program was tied to store personnel compensation and performance review. They needed to recommend the best possible technology, while still getting the most for their investment. The widely accepted and credible legacy solution meant switching vendors and raised questions about whether RetailNext could handle this global retailer’s volume.

The executive team at a national department store wanted to target a specific customer segment with a new private label, with the expectation that it would increase sales. The new label included a full line of women’s ready-to-wear, men’s sportswear, and men’s tailored apparel. But before allocating funds for a national launch, the team wanted more insight into how shoppers would interact with the label and an idea of what they could expect in sales.

100% PURE, a fast-growing prestige cosmetic brand, initially found success through its e-commerce platform. However, as the company expanded its physical store presence, it faced challenges in maintaining revenue and profitability. The primary challenge was to drive customer acquisition and sales more effectively and efficiently to ensure a meaningful return on investment for its brick-and-mortar stores.

Historically, Goodwill Southern California never had a way of accurately measuring visitors – store traffic– to its stores, and as such was unable to calculate basic retailing operational metrics like conversion. Point-of-Sale data pointed out transactions in both units and dollar values, but data was incomplete and out of context without corresponding store traffic data sets. As a result, store operations were left without definitive levers to push and pull upon to deliver results. For example, if a day was unseasonably warm and both sales and sales transactions were down, an anecdotal connection could be made, but without traffic and conversion data, no corrective actions could be planned - it’s not like store managers could affect the weather.

As the vice president of product at Lolli and Pops, Jessica Mennella leads the buying team and is responsible for much of the visual merchandising at the company’s stores across the United States. Each store location has a wide selection of confectionery SKUs, and for its scooped sweets and bulk items, stores have up to 350 bins or more. Re-assorting and setting the stores can be a difficult challenge, involving many meetings with Jessica’s planning team, and often requiring a lot of travel. On a flight from Chicago to San Francisco, Jessica found herself chatting online with her planner. As they discussed 10-15 stores, each with a slightly different layout that had been planned to roughly 80 percent, they struggled to finish and to fine-tune the layout, facing issues like where to visually place dark chocolates in a store filled with sours.

In the fast-paced world of retail sales, the secret to attracting and retaining customers is often highly specific and can change on a monthly, or even weekly, basis. Still, there are fundamental drivers of shopper satisfaction – and dissatisfaction – that lead them to choose one store over another. Luminoso partnered with a survey provider who sent surveys via a mobile app to shoppers as soon as they left a store. The 13,752 shoppers who responded told us why they had chosen to shop at the retailer they did and how they would rate that retailer on a scale of 1 to 5. Their feedback revealed fascinating insights about why they choose the retailers they do, what impacts their satisfaction, and how different leading retailers are perceived differently.

LQA, a luxury hotel assessment company, faced several challenges in analyzing customer feedback. The feedback was largely unstructured and text-based, making it difficult to analyze using standard analytics approaches. The data sources were highly varied, including guest satisfaction surveys, social media reviews, and LQA audits. Additionally, the data volumes were large, with a single luxury hotel receiving thousands of customer communications daily. LQA needed a solution that could scale up or down to meet demand and provide accurate insights despite the unique vocabulary and multiple languages involved in the hospitality industry.

Japanese manufacturer DENSO is the world’s second-largest producer of automotive parts. With over 130 global sites, production line staff perform tens of thousands of maintenance checks and produce over 20,000 maintenance notes each year. DENSO sought to reduce equipment downtime and increase productivity by improving search around maintenance notes. Accessing past notes and relevant fixes enabled production line engineers to repair faster, but it was difficult and time-consuming to search this mass of information. Engineers would submit requests to management, who would manually review past notes to guide repairs. This created a bottleneck, impeding productivity, especially for engineers in overseas factories. DENSO needed an accurate, categorized search system for maintenance notes, a way for line engineers to directly access and search past records, and unification of the note search system across all global sites.

Part of the United States Department of Health and Human Services, the Centers for Disease Control and Prevention (CDC) serves the mission of protecting public health. As pandemics emerge, the CDC relies on predictive analytics models to measure and track the spread of diseases to understand their evolution and impact. The CDC builds sophisticated models based on past quantitative data gathered from doctors, emergency rooms, and urgent care centers. Although reliable, these models are reactive and outdated by the time data is analyzed. To be truly predictive, the CDC needed the ability to understand the spread and severity of a wide range of illnesses, even those that were not yet known. During a recent flu season, the Situational Awareness Branch of the CDC looked to prove the value of analyzing real-time text data to create more accurate prediction models. An effective solution would: Examine public discussion to monitor and predict the spread of the flu, Analyze real-time text against historical qualitative data, Perform advanced analyses on incoming conversational text.

Hilton, a global hospitality leader with over 408,000 Team Members in 113 countries, values its workforce as its most important asset. The company has been recognized as a top employer globally, and it continuously seeks to improve work conditions based on Team Member feedback. However, the sheer volume of survey comments received annually makes it challenging to aggregate feedback and derive actionable insights promptly. Hilton needed a solution to quickly identify top themes, relate comment themes to survey questions, explore insights across different workforce segments, and be user-friendly for both analysts and Team Members.

C Space, a leading customer agency, was tasked by a global pharmaceutical brand to analyze a community of caregivers of individuals with schizophrenia. The challenge was to uncover key issues, communication channels, and resources important to caregivers from nine months of feedback. This feedback included open-ended survey responses, discussion threads, journal entries, and photo comments. Analyzing these diverse datasets as a single corpus was critical for uncovering themes and trends, but it was a complex and time-consuming task. The client wanted to ensure they were not missing higher-level insights by focusing too much on individual data points.

About to launch its first wearable technology product, this leading global consumer electronics brand sought to reliably monitor customer feedback for emerging issues. Given the company’s scale and the launch’s publicity, problems needed immediate detection and resolution. Most solutions the company reviewed required the painful process of manually building and categorizing inflexible lists of keywords or important terms. An effective solution would: Accurately identify, categorize, and label underlying intents in feedback; Quickly uncover and track emerging issues to monitor effectiveness of fixes; Offer support in multiple languages, including English, Spanish, Chinese, French, and Russian.

The Business Analytics team recently undertook a new initiative: to find connections between employee and customer satisfaction. To be specific, are there drivers of employee satisfaction that have an impact on customer satisfaction as well? The Business Analytics team realized that identifying such connections, if indeed they exist, would enable them to prioritize initiatives that would be the most beneficial to both employees and customers.\n\nUntil this point, the team had relied upon rigorous statistical methodologies to analyze predominantly quantitative data. However, the team would need to focus on unstructured data, such as open-ended survey responses, in order to fully understand the key drivers of customer and employee satisfaction. Traditional statistical methodologies, while valuable, are not as effective or rapid at analyzing unstructured feedback.\n\nThe team was also determined to eliminate as much bias as possible from the analytical process. However, traditional approaches to processing text-based data rely extensively on keyword lists and ontologies created by a human analyst. The Business Analytics team was concerned that an analyst might inadvertently skew the results by cherrypicking keywords or being subject to confirmation bias.

athenahealth, a leading provider of network-enabled services for hospital and ambulatory customers, faced the challenge of managing and analyzing large volumes of customer feedback. The Voice of the Customer (VoC) team at athenahealth collects and analyzes feedback from over 160,000 providers, receiving over 6,400 monthly survey responses from various user groups including doctors, nurses, and managers. The datasets were too large to manually review but not large enough for most natural language understanding (NLU) tools. Previous attempts to address this issue included manual reading and summarizing of comments, building in-house tools, using open-source NLU, and conducting two-week 'tagging marathons' each quarter. An effective solution was needed to quickly categorize customer feedback, identify and quantify issues affecting customers, and uncover deeper insights without consuming analyst time on manual work.

MAi Research, a full-service market insights firm, faced the challenge of providing impactful insights to help clients make effective decisions. They needed cutting-edge natural language science to identify target markets and differentiate resonant versus polarizing messaging. Additionally, they required comprehensive drivers analysis to quantify the importance of text and closed-ended responses and simulate the impact of changes to driver performance.

With over 100 million daily active players, this mobile game developer receives up to 120,000 support tickets per day after major updates. In these tickets, players report bugs and ask questions across multiple languages. Due to unique and frequently changing terms used in its games, the company’s keyword matching system lacked the accuracy to route low-impact issues to self-help articles. This flooded Customer Support with questions about minor problems, creating severe bottlenecks and taking time away from critical issues. The company needed an accurate, fast way to identify, triage, and resolve problems reported in tickets. This would not only aid in surfacing emerging issues, but also improve response time by redirecting minor problems to self help so product teams could address the most critical issues, quickly, preventing player churn.

This Fortune 500 financial investment services firm sought to improve adoption of its online customer portal. To increase its adopter base, the firm’s Customer Intelligence Team needed to surface key insights buried in customer data collected from Net Promoter Score (NPS) surveys, to which it received over 9,000 responses each month. With insights in hand, the team could better understand what customers thought of new financial products. This would enable targeted, valuable discussions with management and result in more efficient iterations on product improvements. The 12-person Customer Intelligence Team manually sorted and analyzed all responses. This translated to a loss of at least one full business day each month, totaling about 100 people-hours, in an attempt to simply read feedback. The firm needed to: Quickly process high volumes of data with no maintenance or manual intervention Understand root causes affecting NPS and trends over time Surface key concepts in data for management and product roadmaps

Despite receiving constant customer feedback from its website and call centers, this North American office supplies retailer was blind to its lessons. The insights its Contact Center Team could use to identify, address, and reduce issues that chased away customers were buried within its more than 2.5 million documents. The feedback included text from online chats and call transcripts across multiple contact centers and agents. With thousands of messages and calls each day, in-house solutions could not scale or process enough data in real time – let alone aggregate across channels and sources. An effective solution would: Analyze constant, high-volume, aggregated streams of feedback, Help identify and understand prevalent, critical issues, Uncover insights to improve customer support processes.

Juan and the Orchard ownership group had reached the limit of their own capacity. Orchard faced two key challenges: Limited engineering capacity to proactively manage their properties in an effective manner. A rigorous method to measure success and return on investment, especially for new energy efficiency projects. Moreover, from his position overlooking two hotels, Juan wanted a better way to monitor usage and compare results between the two properties. Each 10 stories tall, with a roughly equal number of rooms and located just 500 yards apart, the energy consumption profile should be very similar. In fact, this wasn’t the case.

Bill DeMartini, Chief Engineer of the W Hotel San Francisco, was searching for a way to identify the unseen problems in his building. He wanted to take energy efficiency beyond his hotel’s LEED Silver certification. To tackle this, he knew he must look at TRACE, the award-winning restaurant. Without a cost-effective way to monitor kitchen operations 24/7, Bill lacked the facts required to optimize his kitchen and improve W Hotel’s efficiency. Bill engaged with Verdigris to deliver the granularity he needed to identify ways to improve the kitchen operations.

Hospital sustainability policies and demand for environmentally conscious healthcare supplies drove Vention to Verdigris. Vention was seeking energy data metrics, particularly validation of energy usage and insights for potential savings. With 13 facilities and a newly formed sustainability council, Vention needed a tool to easily identify and quantify hidden potential cost savings. They also needed a reliable system to track equipment malfunction to maintain sensitive environments which are critical to medical device manufacturing. Vention selected their Sunnyvale office, which has several clean rooms, for the proof-of-concept test. They wanted to learn how to best prevent downtime for important equipment such as HVAC, which could jeopardize FDA-approved clean room conditions, contaminate materials, and ultimately increase production costs. Vention approached Verdigris to get help going above and beyond their customers’ specific requirements for sustainability.

Building owners and property managers face significant challenges in verifying the return on investment (ROI) from energy efficiency measures due to the dynamic nature of buildings. Factors such as shifting weather patterns, changes in occupancy, and varying equipment lifespans complicate the isolation of savings from energy efficiency investments. Traditional methods of verifying savings, such as hiring energy engineers to create predictive thermodynamic energy models, are often complicated and expensive. These methods involve intensive data collection and calibration processes, which can cost between $0.10 to $0.50 per square foot, making them cost-prohibitive for many building owners. Additionally, traditional methods have limited capability for tracking ongoing performance, which is crucial for identifying failures or below-average performance of energy efficiency measures.

Essent, the largest energy company in the Netherlands, wanted to enhance its customer experience and increase sales by offering a call-back service to consumers looking to purchase gas or electricity. However, the challenge was to implement this service in a cost-effective manner. Static call-back options were not efficient as they were visible to all online visitors, including those who did not need offline assistance, leading to unnecessary costs. Essent needed a solution that could intelligently identify and engage only those customers who would benefit from a call-back, thereby optimizing resource utilization and improving sales conversion rates.

Three, a leading UK telco, sought to enhance its contact channel operations to deliver a better customer experience, increase profits, and empower its agents. The company aimed to leverage technology to identify and facilitate growth within their multichannel team, aligning with their innovative business practices.