The company traditionally used its in-store inventory to fulfill online orders, but management thought this ship-from-store program may actually be hurting sales by causing out of stocks for in-demand products.

The insurer had recently launched a media campaign (including digital, radio, direct mail, and TV) in several markets as the management team believed that increased investment was needed to drive growth. However, after the campaign was in market, management had a difficult time disentangling the success of different elements of the program and understanding in which locations the program was successful.

A leading P&C provider analyzed its product line and discovered that pricing was out of kilter with customer risk levels. Lower risk customers were paying more than their losses warranted, and higher risk customers were paying less. The company believed that better aligning customer pricing with customer risk would create a more resilient product line. The company’s best customers would pay less and therefore be less exposed to potential competitive pricing pressures, whereas more risky customers would pay more to cover the cost of their potential losses. The company was concerned, however, that a wholesale change in its pricing structure might lead to unexpected reactions from the customer base. Rather than simply rolling out the aggressive pricing and rating changes that strictly aligned pricing with risk, the company tested two versions – Option A, an aggressive price change and Option B, a moderate price change.

A global multi-brand hotel company with a large loyalty program introduced an accelerated loyalty promotion, in which it offered select members accelerated tier advancement if they stayed at least three times during a 90-day promotional period. The company expected the offer to drive stays in the short-term, but wanted to measure the long-term impact of the loyalty offer. The hotel sought to understand whether members had incremental stays after the promotional period or if they simply reverted to the stay behavior they exhibited prior to the offer. The company sought to deploy this offer to a limited subset of members given the costs (such as free nights) associated with giving guests higher loyalty status. Further, the hotel wanted to avoid over-diluting the standard path to achieving this top loyalty status. As such, the company planned to limit distribution of this offer to the 5% of customers within its mid-level loyalty tier that were predicted to respond best to the program, but struggled to confidently identify those top customers.

A global hotel company launched a limited time promotion offering a 10% discount to guests staying two or more nights. The company sought to better understand the effectiveness of this promotion by measuring the overall incremental profitability in light of three types of guest stays that would qualify for the reward: Incremental Stays, Upsells, and Accidental Qualifiers. Incremental Stays were guests who would not have otherwise booked a stay during this period but were called to action by the promotion, offering the highest incremental gains. Upsells were guests who stayed an extra night at the hotel to qualify for the promotion, offering moderate incremental gains. Accidental Qualifiers were guests who did not reserve or extend their stay because of the promotion but benefited from the discount anyway, representing a loss for the company because they received a discount not tied to any incremental profit.

Management was concerned that a popular but aging brand’s financial performance was on the decline. The target guest segment’s perceptions of the brand were not aligned with the brand’s aspirations, and management believed that an inconsistent guest experience was damaging the brand’s preference and performance. As this concern grew, management developed a strategy to relaunch the brand, which included significant investments in renovated lobbies and other visible features, as well as retraining hotel staff to achieve a high quality guest experience. However, management’s early attempts to quantify the impact of these expensive changes produced inconclusive results and led to disagreement among decision makers.

Management believed that a core brand underperformed, in part because of its outdated brand offerings. The hotel chain designed a hotel refurbishment program and began investing hundreds of thousands of dollars per hotel to update lobbies, guest rooms, and exterior signage. The brand’s management carefully tracked the performance of the initial set of “pilot” hotels but was unable to detect a clear impact on key performance metrics. Measured changes in RevPAR, occupancy, and customer satisfaction varied widely, even erratically, across the refurbished hotels. Management halted the remodel investment and reconsidered various options to address the brand’s underperformance.

As a major credit card issuer, the bank frequently launched direct mail campaigns to spur customer acquisition. It targeted its mailings to customers that management believed would be more receptive to various card offers, but card acquisition had plateaued in recent months. Further, the bank had a difficult time isolating the true impact of each campaign amidst numerous outside factors. Management was struggling to confidently answer the following questions: How much incremental revenue do our campaigns drive per customer? Which features of each offer are most successful? Which customers will respond best to which offers? The direct mail campaigns were a significant investment for the bank, and without a full understanding of each campaign’s true ROI, there was uncertainty about how to best allocate resources. The bank had three key cards it wanted to promote: Card A: No annual fee, 1.25 Miles; Card B: Annual fee, Double Miles; Card C: 0% APR for 12 months. Management hypothesized that each card might appeal to Money Market customers in different ways, and the bank wanted to launch direct mail campaigns for each. Before moving forward, the bank needed to understand if the offers would generate positive ROI, which Money Market customers to target each offer to, and how to tailor each offer to maximize profitability.

The company was interested in using digital media to drive new customer acquisition but they were unsure if their investments would generate high ROI. The bank had tested online ads in some markets, but due to weekly volatility, impact of local market conditions, and other external factors, the client had a difficult time isolating the cause-and-effect relationship between online ads and new account generation in the branch.

The company provided a variety of different types of credit cards, and each card had different rewards programs designed to increase customer spend and engagement. While analysts did have access to advanced customer data, it was difficult for them to isolate the true causal relationship between different rewards offerings and customer spending. The company aimed to answer the following questions: Were rewards incentivizing customers to increase their card spending? Or were they merely subsidizing customers that would have reached various bonus thresholds of spending without incentives? Which cards were generating enough spend to justify the costs of reward redemptions? Which rewards were most effective with which customers? How could they target their offerings to maximize ROI? Amidst this uncertainty, the card provider wanted to introduce a new product to its portfolio. There was internal debate about which of two rewards programs would be more successful: a Double Rewards Points program, or a Cash Rewards offering. The programs were projected to have similar redemption costs, but the company needed to better understand the in-market impact of each offering on customer behavior before it could confidently choose between them.

A large chain of auto service centers wanted to use coupons to drive traffic to its locations. The company offered targeted coupons for its wheel alignment service, redeemable during a month-long period. The challenge was to determine if the promotion drove incremental transactions or merely subsidized existing purchasing behavior. The inherent noise in daily invoices data made it difficult to isolate the coupon's impact, complicating the understanding of whether the coupon was driving new business or just giving away money to customers who would have purchased anyway.

The client, a leading US automaker, sought to understand the role of digital advertising in its overall marketing strategy. They aimed to evaluate the ROI of online marketing activities compared to traditional channels, focusing on a specific car model. The challenge was to isolate the true incremental impact of digital marketing efforts amidst the noise in sales data from individual zip codes. The client turned to APT’s Test & Learn software to analyze the effectiveness of online digital spend in driving incremental sales.

The retailer’s merchandising team wanted to optimize its product assortment within the brakes category. As a part of this effort, the retailer planned to introduce an economy brake kit. However, management was unsure how to accurately quantify the total store impact of the new product, net of potential cannibalization effects on the existing brake product line.

Executives at a leading auto service chain wanted to identify the optimal operating schedule for its locations. Given heavy customer traffic on the weekends, management planned to open its service centers two hours earlier on Saturdays, but first wanted to understand the total profit impact of the change. Specifically, the client sought to understand if the extended hours would generate a sufficient increase in sales to offset the associated costs.

The automotive industry faces a significant challenge in accurately attributing car sales to digital marketing investments. Both digital marketing providers and auto manufacturers need to confidently attribute sales to different marketing channels to ensure optimal allocation of advertising budgets. This challenge is compounded by the fact that consumers cannot purchase vehicles online, making it difficult to measure the direct impact of digital marketing efforts on car sales.

The biggest pain points today’s sales organizations face are lack of visibility in pipeline movements and inaccurate sales forecasts. Compiling that level of analysis can be both arduous and time consuming and takes resources from the primary goal – selling. For Jake Hofwegen, Vice President of Global Sales Strategy & Operations with Websense, the intelligent data from C9 was not only eye opening and crucial for his business, but his newfound accuracy came without excessive man-hours spent manually compiling forecasts. Websense, a global leader in unified Web, data, and email content security, faced challenges in managing pipeline visibility and forecast accuracy. The company’s sales strategy involves distribution through a global network of channel partners and delivered as software, appliances, and Security-as a-Service (SaaS). Managing that level of pipeline management and forecast visibility for a flourishing sales organization requires a balanced approach of strategy and depth.

Research has shown that forecasting is a thorny issue. In a survey of 1,700 companies, research firm CSO Insights found that only 46% of forecast deals ended up in the “win” column (CSO Insights “2013 Sales Management Optimization Study”). Lack of accurate sales metrics means that executives are stuck leading and managing based on hunches and instinct. Before establishing a relationship with C9, the sales team at Yahoo had a fundamental lack of trust in forecast numbers. Pipelines were plagued by excessive subjectivity, and rampant “shadow accounting” in Excel spreadsheets was common. Overall, unnecessary friction and poor communication existed between management and the sales team. “We had the same conversation each week, arguing about the numbers,” says Patrick O’Leary, Yahoo Senior Director of Sales Operations and Effectiveness. Problems at the Rep Level: Because they lacked well-defined forecasting metrics, salespeople had trouble following a consistent and disciplined selling process. Salespeople spent an inordinate amount of time each week gathering information to create their forecasts; this pulled them away from core selling activities. Problems at the Management Level: Sales managers found it cumbersome and time-intensive to cope with shadow-accounting practices and consolidate information from many spreadsheets. Confusion around reported numbers made it difficult to gauge the actual status of deals and their probability of closing. Problems at the Executive Level: Lack of faith in the accuracy of forecast numbers made it difficult to make strategic business decisions. Forecast data was vulnerable to holes and inaccuracies whenever someone was hired or fired, which made it difficult for executives to balance downstream business operations.

Progress Software faced challenges despite having a mature pipeline management process. They needed more robust trending and pipeline best practices than what their current CRM system offered. Additionally, they required advanced analytics support to standardize their sales processes across the globe.

Brainshark, a leader in on-demand presentation solutions, faced significant challenges in pipeline management and forecasting due to the complexity of their sales model. The company employed multiple teams and opportunity types, making it difficult to gain meaningful visibility from their CRM system. This lack of visibility resulted in frustration and inefficiencies, as sales operations had to manually map changes across various sales stages. Additionally, as a SaaS provider, Brainshark needed to keep sales and marketing costs low while building scalable process efficiencies. The company had to decide whether to build a custom sales analytics solution or buy an existing SaaS solution to address these challenges.

Continuous casting presents numerous variables that need to be analyzed to improve technology and advance the boundaries of steelmaking. The process involves transforming a constant stream of liquid steel into endless strands of solid metal, which requires precise control to minimize waste and improve yield. The challenge is to understand and simulate the complex processes involved in continuous casting, including fluid flow, solidification, and mechanical deformation, to achieve superior quality and cost efficiency.

A Fortune 100 Insurance and Finance Company needed a better way to manage their business intelligence (BI) dashboards. The Financial Planning & Analysis (FP&A) team for Life & Retirement Division spend upwards of 80% of their time building and explaining dashboards. The content they produce is heavily focused on the 'what' rather than the 'why' or 'how' of an analysis. This leaves plenty of room for ambiguity and additional questions. Reporting historically occurred in spreadsheets, slides, and similar forms of manually-generated content. As a result, the analysis is not only time-consuming, surface-level, and generic; but was unable to scale with the demands of increasing data.

Translating enormous amounts of survey and market data into dynamic compensation profiles is a challenging feat. Instead of solely presenting this data in charts and graphs, the company realized it needed to convey the information in an engaging, SEO-friendly way in order to drive site traffic and conversations. It needed narratives.

The Analytics team developed a comprehensive dashboard to inform and highlight business performance metrics across the Customer Service team. Visible by everyone from Executive leadership to floor managers, the dashboard includes reports on the overall business summary, trends, KPI performance, and more. For some, the visualizations on the dashboard were difficult to interpret, and the champions on the customer service team were spending a significant amount of time analyzing the data and explaining key takeaways to different departments.

South Calgary Primary Care Network aims to deliver better health outcomes by ensuring patients receive the right care, from the right health professional, at the right time. To help employees improve service levels, the network provides employees with Qlik visualizations that highlight important metrics such as number of referrals and patient wait times. However, because visualizations can be difficult to interpret, the network’s analytics team spent a significant amount of time explaining key takeaways to different departments. The network realized it needed to communicate these insights in a more scalable way.

Wellington Free Ambulance (WFA) faced a challenge in making data visualizations from Qlik Sense easily interpretable for individuals without an analytical background. The data analytics team spent hours each week writing commentary to explain key takeaways from charts, graphs, and tables. This process was time-consuming and inefficient, and the team sought a way to provide in-depth information more efficiently while ensuring that the insights were accessible to both internal and external stakeholders.

Dominion Dealer Solutions needed a cost-effective, automated way to take information from multiple sources, quickly and accurately analyze the data, and generate consumer-ready automobile descriptions in real time. The challenge was to create engaging content that could drive click-throughs, showroom conversions, and vehicle sales. The existing manual process was time-consuming and prone to errors, making it difficult to scale and meet the growing demands of the market.

The client, a major e-commerce company with over 70 businesses, faced significant challenges in providing personalized recommendations to their affiliate marketing clients. Despite having large amounts of campaign data, Account Managers struggled to collate and present this data in a digestible format, especially for 'self-service' clients who received little to no support from the client services team. This lack of personalized insights made it difficult for these clients to optimize their campaigns effectively, leading to lower engagement and retention rates.

The CFO of a large telecom provider was looking for areas to automate business processes to cut costs and drive operational efficiencies. Reporting processes, in particular, were extremely time consuming and limiting to the growth of their business. They approached Narrative Science to better understand how Natural Language Generation (NLG) was being used by Financial functions to drive digital transformation.

A leading financial services company needed to streamline the production of investment research analysis. The company had a high volume of companies requiring research and needed to maintain a consistent voice and level of detail across all research. The existing process was time-consuming and resource-intensive, limiting the firm's ability to provide comprehensive coverage and high-quality insights. The firm sought a solution to automate and enhance the production of investment research to improve efficiency and consistency.

As a large company, Deloitte often faces data overload: access to multiple, very large data sets without the time and resources to fully analyze that data for meaningful recommendations. Company leaders often must move quickly to make decisions based on data findings – a process that can often require manual or semi-manual data aggregation and analysis. Adding to the need for quick data analysis are the needs of Deloitte’s clients, who often approach Deloitte with a desire to innovate their businesses. Automating manual, time-intensive processes to derive insights from large data sets, or using data to create new ways to engage with rapidly-changing consumer bases, adds value for Deloitte’s clients. In order to show value to clients quickly, timelines are often tight.