The grocery chain wanted to use their data to understand their customers better and maintain their market-leader position. They decided to align their merchandise mix and store inventory to match each customer’s specific needs. With hundreds of stores, thousands of products, and daily visitors, the store had details on almost 140,000 transactions per day and wanted to use this data to support their business decisions. However, their current environment could not handle the data scale and complexity, making it impossible to conduct Year-over-Year, much less Month-over-Month analysis. They resorted to writing complex queries using Impala to fetch data from their Cloudera platform. But when they tried to join tables with more than one billion cardinalities, Impala usually timed out.

The pharmacy store chain, with over 20,000 national and international suppliers, was struggling with the efficiency of its supply chain network. The company had billions of records from over 50 tables and sources, making analysis difficult. There was no single source of truth as data was decentralized and coming from different sources. Reporting was time-consuming as teams had to compile reports manually and could send them only once a month to top 200 suppliers. There was no way to provide secure, self-service access to massive volumes of supply chain data to external users/suppliers.

The telecom company was facing several analytical challenges. The time-to-insights was too long for ad hoc analysis, they first had to write, tune, and optimize their queries and then run them to get the required reports. As massive datasets were joined at runtime, queries would take 10 to 30 minutes to return, and complex ones would take even longer. As the business grew, it became difficult to meet the aggressive timelines of its users. In addition, though their data platform allowed them to retain 15 months of data, it was very challenging to analyze across multiple months due to the volume and complexity of their data.

The global telecommunication company was facing several challenges with their existing MicroStrategy BI tool. As the volume of their data increased massively, and more dimensions, metrics, and measures got added to their MicroStrategy reports, response times increased from seconds to several minutes, even for smaller and less complex reports. Transferring billions of rows of raw data from the data lake to MicroStrategy for computations caused the application to time out in most cases. Disconnects between the data collected by different departments such as marketing, finance, product development, and others made cross-functional analysis extremely difficult. Building cubes on MicroStrategy consumed 50% of resources, resulting in prohibitive costs. They also faced inconsistent response times on MicroStrategy queries and an inability to meet increasing business requirements as they could not accommodate additional dimensions and measures in reports without impacting performance.

The e-waste recycling company was facing several analytical challenges on their Snowflake / AWS / Tableau platform. They were experiencing severe degradation in query performance while analyzing multiple years of data in Snowflake. There was no semantic layer to define consistent data models and standardize KPI calculations. The company was also dealing with unpredictable querying costs on their Snowflake data warehouse, with bills running very high at times. They found it difficult to model their data and deal with multi-level hierarchy and one-to-many joins between facts.

A leading multinational investment bank and financial services company operating in 50 countries wanted to establish an executive-level global view of its financial data to support Management Discussion & Analysis (MD&A) for their C-suite. Their executives needed a single dashboard where they could measure the company’s performance on a comprehensive set of real-time measures and drill down to the lowest levels of granularity instantly. They also wanted to enable quick ad hoc analysis for 700 analysts located across the globe. Their reporting requirements were quite complex, and they needed a single view across all international businesses. It was difficult to use their existing BI environment to model complex financial KPIs and build reports on massive financial records. With no tolerance for latency, it became challenging to meet the high expectations and aggressive timelines from the executive team.

The US-based financial technology services company was struggling with creating and automating a single consolidated view of all its data for hundreds of users. Despite having uniform data, reporting was siloed due to the inherent limitations of the analytical environment. They initially attempted to build MicroStrategy intelligent cubes from data residing in Google BigQuery. However, there were limits on the amount of data each cube could hold. As a result, they were forced to split their data and create separate cubes for combinations of geographic regions and data sources. This led to an inability to get a single consolidated view of their data as they were using 20 different cubes for core reporting. The time to publish the cube, even for a single data source, exceeded 6 hours. Changes in data or incremental refreshes required full reprocess downtime. Reprocessing took over 24 hours, and users could not access the system for this duration. They maxed out the capacity of their Google environment. Despite running on a large server, there was no room for additional data. They were spending massive amounts on cloud computing.

The multinational computer software company wanted to consolidate the data from different customer touchpoints and create a 360-degree view of the customer interactions with its products and services. They faced several challenges in analyzing the enormous amount of data being generated. A wide variety of customer data from sources such as call centers, web interactions, customer churn, marketing, purchase, and product usage made it difficult to get a consolidated view. Disconnects between the data collected by different departments made cross-functional analysis difficult. Non-standardized reporting between business units, with over 1000 analysts reporting on 80 customer metrics collected from more than 20 source systems. Different business units used different BI tools and were reluctant to adopt new tools.

The telecommunications company had access to a massive amount of session data from live TV viewing, DVR, video-on-demand, pay-per-view, set-top box usage, and other streaming devices. However, it was difficult to connect and get useful insights from this data. The existing infrastructure worked well for small datasets, but as data volumes grew, they started facing challenges in processing and deciphering it. The company was unable to leverage the massive volumes of viewership data from millions of subscribers stored on Hadoop to improve the viewer experience and gain a competitive advantage. New or ad hoc queries took hours or even days to return, making them unusable for decision-making. Slow responses to queries made it impossible to analyze data over extended periods to understand trends or recognize patterns.

HotelTonight, a last-minute travel service, collects large amounts of accurate data on hotel inventory, then connects available rooms to consumers at the exact moment the need arises. As a fast-paced business dealing in highly complex data, HotelTonight needed on-the-spot data analytics. Sales, product management, and account management staff had logical questions. What is the right price point for a given hotel in a given region? How many rooms are likely to be available during a particular event? The answers lay in the company’s database, but couldn’t be accessed through any natural-language discovery process. Most of HotelTonight’s data is transactional. Before Looker, they used a MySQL database to track variables such as pricing, hotel availability, rooms secured, and customer activity. They could only analyze data by running manual queries and reports, or through a complicated Ruby on Rails document management system—both time-consuming and inflexible processes that required engineering support.

ThredUp, an online consignment shop, faced challenges in managing its complex data environment. As a large-scale aggregator of used one-off clothing items, they had a huge number of SKUs and a very broad supply chain. The company's distribution center collected information as clothes were inspected, itemized, categorized, packed, and shipped. By analyzing that data and integrating it with sales and marketing data, company management could discover which types of inventory were most valuable, how best to ensure quality, and how to increase transactional volume. However, their existing data management system lacked the power and flexibility to grow with the business. They needed a business intelligence system that could provide full, real-time analytics to anyone with an account login and would enable users to collaborate on iterative exploration of a shared data repository.

Kiip, a mobile rewards network, needed to monitor large amounts of dynamic data about app users and their in-app activities. The company's success depends on their ability to target audiences with prizes at specific moments within the app environment. Accurate data on user demographics, reward redemption patterns, and other consumer behaviors enables them to provide app users with an enjoyable experience, which results in strong advertising performance. However, the makeup of those users can shift dramatically as mobile apps rise and fall in popularity. At the same time, different apps appeal to different types of users, and the nature of user activity within an app may also change due to variables such as time of day, app promotions, and external events. Prior to working with Looker, Kiip faced several challenges on the data analytics front. Each time the business team wanted detailed information, they had to formulate a request, send it to a data analyst, and wait for a report to answer their questions. This meant that the company’s analytics team was over-burdened with query-writing and report management, instead of focusing their talent on growth initiatives and innovations around data. And because Kiip had no central data repository, the company also found that shared information wasn’t consistent across all office locations.

Frank & Oak is a lean, early-stage company that strives for maximum efficiency to fuel its rapid growth. A single business intelligence specialist supports a workforce that is expanding to more than 100 people, with analytics requirements that are more challenging than those of a typical retail environment because of the monthly introduction of new collections and the additional tracking required to manage the subscription business. The company started out with a set of discrete internal SQL databases, including a Magento e-commerce database, a custom inventory (warehouse) management system, and a database for web event tracking. In addition, data resides in external sources, such as Desk.com for customer service, MailChimp for email marketing, Google Analytics, and Google AdWords. Before Looker, the data analyst ran manual queries upon request, extracting data from various sources and exporting it to Excel for analysis. Because nontechnical users had no ability to explore or refresh data on their own, waiting for custom queries could create problematic bottlenecks. Also, the specialist spent a disproportionate amount of time writing basic SQL instead of doing the advanced analytics that drive real value for the company.

Simply Hired, one of the world's largest online job search engines, manages a significant amount of data to operate its search engine. The company captures user-generated data from every transaction, resulting in a massive amount of data that both business and technical staff want to analyze continuously. They rely on this data to improve search algorithms, optimize website and other channel interfaces, link cost-per-click and sponsored listing costs to market-driven auction rates, and strengthen online campaigns through regular A/B testing. Data analysis is also essential to predicting trends and identifying new business opportunities. Before Looker, Simply Hired engineers handled analytics by pre-aggregating data from multiple data sources and importing it into MySQL. Users couldn't see the connections between different data sets or understand what was going on at a high level in the business. Only people who knew how to write SQL could write queries, blocking an entire population of business users from direct data access.

Trumaker, a men’s clothing brand, was facing data chaos due to its distributed model where stylists throughout the nation provide custom wardrobe advice and services. The company was accumulating a lot of data from multiple departments, including heavy operations and overseas manufacturing facilities. This created numerous bottlenecks as data analysts struggled to keep up with the demands of a rapidly growing, complex e-commerce service operation. They needed a solution that could track as much data as possible without creating burdens on the report system.

StraighterLine, a provider of self-paced college courses, was looking for a way to engage with students and prospective students in a familiar channel. They wanted to provide engagement options for mobile users and improve conversion and customer satisfaction rates. The company had a live chat solution from LivePerson, but it was not being used to its full potential. They were completing around 100 chat conversations per month, with inquiries fielded on an ad-hoc basis by members of the telephone call center team. They realized they were missing substantial opportunities and needed to optimize their live chat.

Discount Tire Direct, a subsidiary of Discount Tire Company, was facing challenges in delivering an outstanding digital customer experience and increasing e-commerce conversion rates. The company wanted to maintain high in-store customer satisfaction rates via digital channels. They had experimented with an on-premise live chat solution when they initially added e-commerce to their website in 2003, but it was simply a passive button called Click to Talk and was soon removed. In late 2011, they decided to revisit the use of live chat deployment on their website to rethink how they engaged customers on their website, seeking to make it the best buying experience possible for their customers.

The Association of Certified Fraud Examiners (ACFE) was seeking a clearer channel for communication with its international members. The organization was facing challenges in facilitating communications with international members through a written, real-time channel. They also needed to provide service for members and prospective members who might not call in. The ACFE was looking for a solution that could improve communication in situations where language barriers and poor connection quality were hindering effective customer service. They also recognized that members in North America would benefit from a digital engagement channel, as it would save time and provide a convenient alternative to phone calls.

Moosejaw, an outdoor retailer, faced several challenges. They wanted to humanize the digital experience, deliver real-time online customer engagement, drive digital conversion rates, and engage customers at high-impact moments. As an early adopter of the Internet, Moosejaw understood the importance of digital engagement and implemented a live chat solution powered by LivePerson. However, they wanted to evolve beyond chat to digital engagement and become more strategic in how they used the LiveEngage platform. They also wanted to replicate the in-store experience onto the website and find ways to engage with customers through additional channels.

Home Properties Inc., a real estate company with over 40,000 housing units in nearly 120 apartment communities across the Eastern United States, was looking to provide an additional engagement channel for sales and service. They wanted to make it as easy as possible for anyone looking to contact them either because they’re already a customer, or because they want to be a customer. The company had previously deployed a live chat solution in 2006, but the system was often down and the supplier's team was not very professional. This led to the need for a reliable live chat solution with professional support.

Parcel2Go, the largest online package delivery service in the UK, was struggling to provide support to customers on nearly 2,000 parcels delivered each month. With 10 agents, customers would often have to wait in lengthy phone queues before their question could be answered or their issue addressed. The volume of inquiries they were receiving via the telephone were extremely costly, at £4 per call. The company’s customer service channel costs were higher than desired. Parcel2Go sought a cost-effective, online customer service channel to efficiently absorb the high volume of customer queries and provide an improved level of service and support.

Mercer County Community College's MercerOnline distance education portal was receiving a high volume of emails and phone calls from students with routine questions about courses and the registration process. The small team administering the portal was struggling to manage the volume of inquiries, and there was concern that students might be discouraged from registering due to the difficulty in getting their questions answered. The team needed a solution that would allow them to handle inquiries more efficiently and provide a more engaging experience for students.

The consortium of libraries in Ontario was looking for ways to keep libraries relevant as pillars in their communities, and inevitably that means connecting online. They wanted to reach library patrons where they are with research help and other information and boost the number of patrons served per staff. The traditional method of accessing library services required walking into a physical library, which was not convenient for all patrons. The consortium sought to expand access to library research services.

ICON Health & Fitness, a leading name in the fitness industry, was facing a challenge with high abandonment rates across its web properties. Despite attracting millions of visitors to its brand websites each month, the company's online retail performance indicated surprisingly high abandonment rates. The team had difficulty pinpointing the exact reasons for the low conversion rates. They realized that in order to grow ICON’s direct-to-consumer retail business and drive revenue via online channels, ICON needed to implement a real-time engagement strategy that would identify the visitors most likely to benefit from help during their buying process, reducing overall site abandonment and successfully convert prospects into satisfied, long-term customers.

Warehouse Skateboards, a premier online retailer of skateboards, skateboard accessories and skate clothing, was facing a challenge with the costs of engaging their customers via an 800 number. The phone bills were eating up all of their profits and they needed to find a way to move those conversations away from the phone. The company was also facing issues with their website's user interface and content, which were not reflecting how their customers shopped and thought. They needed a solution that would not only reduce their customer service costs but also improve their website's user experience and conversion rates.

Frank Myers Auto Maxx, a car dealership in North Carolina, operates by appointment only. While their website provides a wealth of information about the dealership and its offerings, customers looking to schedule an appointment for the next day would be out of luck if it was after the business's closing time of 7:00 p.m. As a small business, Frank Myers Auto did not have the resources to stay open late and take customer calls throughout the night. The dealership realized it was missing valuable opportunities to interact with potential prospects and customers after hours.

Bankwest, a subsidiary of the Commonwealth Bank of Australia, is a bank established in 1895. It is leading the way in digitally transforming their customer experience and their contact centre. They have been a client of LivePerson for over 6 years and are constantly adapting and driving adoption through web chat, mobile chat, and now In-app and Web Messaging. With a clear digitally focused strategy, having Messaging and Conversational design both playing important roles within that space, Bankwest puts the customer first and strives to offer a world-class experience. They saw the market evolving, their customer’s preferences changing, and strived to lead in the contact centre space. They rolled out In-app Messaging across iOS and Android, then replaced their web chat offering with authenticated asynchronous Web Messaging.

Open Universities Australia (OUA) was facing challenges in providing around-the-clock coverage to its international audience to increase revenue. They wanted to increase entry points and channels for prospective students to more easily engage with the education aggregator. Their long-term goal was to enhance automation to match the value of human agents. To solve these challenges, OUA created a roadmap of automation use cases over the past two years.

David’s Bridal, an international wedding dress destination, was facing a unique challenge. When shopping for wedding dresses, 50% of brides were calling stores to book appointments. If the stores were closed or sales agents couldn’t get to the phone in time, they were losing business. Agents sometimes had to step away from the brides they were helping to answer the phone, creating a poor experience for in-store customers. The company wanted to be available for customers 24/7 without having to add additional staff. They believed AI and automation could help and began looking at different chatbot providers.

Commonwealth Bank, a leading provider of integrated financial services in Australia, was looking to improve customer experience and support, increase conversion rates, and improve operational efficiency. The bank had initiated a live chat pilot in 2009 to differentiate its brand and make it easier for customers to complete mortgage and other banking applications online. However, the bank had reached a point of diminishing returns with its existing rules for customer engagement and needed to rethink its approach to targeting customers.