LPK was outgrowing its industry-specific software, which lacked systematic controls and sophisticated tools for reporting and analysis. The existing software performed well in many aspects but was inadequate in terms of reporting and analysis tools. Additionally, support for the system was available from only one source, which limited LPK's ability to get timely assistance and updates. The company needed a more robust and scalable solution to handle its growing needs, especially in terms of project management and financial reporting.

In the past, KYOCERA was using Maximizer CRM. There were many issues with Maximizer, primarily lack of connectivity and integration. Different segments of the company were not able to see each other’s work, and they lacked the ability to communicate effectively with offices in other countries. The KYOCERA sales cycle was complex, with multiple reps, some working outside of their CRM System, making it hard to create a pipeline of leads.

With growing client needs and the rapid changes in the marketplace, Kay Holdings felt their existing CRM system was falling behind. Their existing system lacked the ability to integrate with Microsoft Outlook, which was a major concern to the company’s management team since it was impossible to control, and track how employees were accessing and leveraging the various modules of integrated data available. Keeping everyone aligned from a business process standpoint while growing and cultivating new business was a challenge. Kay Holdings’s management team wanted to track how they were performing against their business objectives and client services. In the past, the company focused primarily on wealth management, but now they wanted to expand their services to include investment management as well. With the previous processes in place, there was no centralized repository for information and no way to track the history of internal communications. This meant the company’s COO had to spend time meeting individually with members of the firm to get the required information. This detracted from the strategic growth of the company, which included generating new clients, products and services. Kay Holdings need to send reports to their clients much faster. More specifically, having current data and portfolio values daily was a critical business priority.

Despite its successful track record with clients, GSG struggled to get a real-time understanding of its own business operations. The company had no dedicated salespeople, and senior VPs were responsible for both selling and delivering work, making it difficult to disentangle forecast sales from work in progress. This issue was particularly problematic during year-end budgeting and forecasting. GSG had been using Salesforce since 2008 to manage sales deals, but Salesforce lacked out-of-the-box project service automation capabilities, such as billing and time and expense tracking. Employees had to manually copy and paste Salesforce data into spreadsheets to generate reports, which was inefficient and error-prone. Additionally, Salesforce announced a price increase, which further diminished its value proposition for GSG.

The Gorilla Glue Company was experiencing rapid business growth, leading to exponential data growth and new data management challenges. They needed to adjust their processes to better manage their data, inventory, and customer information. The company sought external expertise to identify inefficiencies and areas for improvement to support their continued growth and evolution.

Avaya faced significant challenges in managing its sales forecasting and pipeline processes. The company lacked a standardized approach, which led to inconsistencies and inefficiencies across its global operations. The existing Salesforce Custom Forecasting capabilities were insufficient to meet Avaya's enterprise-wide requirements, particularly in providing visibility into deals that were pushed out. Additionally, Avaya needed custom rollups to offer role-specific views of active opportunities, which Salesforce could not adequately support.

inContact faced several business challenges that hindered their decision-making capabilities. Firstly, Salesforce could not provide trending and comparison of pipeline status over time, which is crucial for understanding the dynamics of their sales pipeline. Additionally, they needed to provide detailed insights behind 'What’s Changed' and trends to make better decisions. Another significant challenge was the inability to report across custom objects in Salesforce and produce unified reports across multiple data sets. This lack of comprehensive reporting made it difficult for inContact to have a holistic view of their sales performance and trends.

StrongView, a leader in email, mobile, and social media marketing solutions, faced the challenge of competing against larger, more established competitors. The company needed a cost-effective way to provide 'air cover' to Sales by educating prospective customers before engaging with their target accounts. The Marketing team had to change misconceptions away from a legacy business model and required a solution to attract and drive prospective customers to their website.

Atmel Corporation, a designer and manufacturer of semiconductors and microcontrollers, faced the challenge of engaging diverse target audiences across multiple verticals such as automotive, medical, consumer, and industrial. The competitive nature of the industry necessitated a more effective digital marketing strategy to drive engagement among vertical-specific accounts. The Atmel marketing team aimed to enhance their digital marketing initiatives to better reach and engage their target accounts, ultimately driving more sales opportunities and conversions.

B2B companies are rapidly adopting technology and data to drive marketing strategies such as account-based marketing and tactics like targeting and personalization. However, many B2B marketers, despite being aware of these concepts, struggle with implementing and integrating them effectively. Digital display advertising, in particular, has been a challenge for B2B companies due to its often ineffective and frustrating nature, with little ability to track or quantify its impact. This has led to a desire for greater capacity to target and personalize digital advertising to improve engagement and effectiveness.

DocuSign, a leader in eSignature transaction management, faced a challenge common to many B2B companies: potential enterprise buyers were visiting its site but leaving before accessing the most relevant content. The company needed to attract the right audience and serve engaging content to visitors. Specifically, DocuSign aimed to drive more traffic from accounts most likely to buy, increase click-through rates to high-value, form-gated content, and boost the conversion percentage of those reaching forms without sacrificing important information.

OutSystems is in a rapidly growing industry and needs to continue its own rapid advancement while facing increasing competition. To achieve this, they must focus on acquiring new accounts and expanding business with current customers. Their solutions are evaluated by technical team members but ultimately purchased by business stakeholders, necessitating a way to reach multiple stakeholders within an account.

Optymyze, a global provider of enterprise cloud applications and services, faced challenges in reaching their target accounts through digital advertising. As an ABM-first marketing team, they aimed to build awareness and credibility within their target accounts and support sales throughout the funnel. However, the specificity of ABM made it difficult to reach the right audience. Additionally, there was uncertainty about targeting the correct individuals within large enterprises, where only a few employees might be relevant to their efforts.

CEB, formerly known as Corporate Executive Board, faced a significant challenge with their form submission pages. The forms required too many fields, resulting in low completion rates. This was a common issue for many B2B companies, as they struggled to balance simplifying forms to promote conversions while still gaining quality customer intelligence. According to Brian Conway, director of marketing operations for CEB, research suggested that today's buyers are typically 57% of the way through the purchasing process before they contact a supplier. To maximize every potential customer engagement, CEB needed to reduce form friction to promote conversion and acquire as much intelligence as possible without forcing the prospect to volunteer all of it. Initially, CEB tried to eliminate form fields in hopes of increasing conversions. While this led to a moderate increase in form completions, it resulted in missing out on important firmographic data needed to properly segment and route the incoming leads.

Product complexity, like data complexity, can become a serious liability as companies strive to profitably meet customer demand. If you don’t know what’s in your data or what your customers really want, you end up guessing and that gets expensive. At Ditch Witch, longer customer lead-times – from 30 days up to six or eight months – were a direct result of this proliferation in product variety. Jacky Williamson, Pricing, Product Structure and Target Costing Manager for Ditch Witch, describes the “spec creep” phenomenon they were facing: “We’d think of an option and just add it to the product, and we’d keep adding and never delete anything. We felt like we were taking something away from customers if we removed an option. With so many options, our planning was poor, leading to longer and longer lead times.” The proliferation in product features made managing, planning and forecasting demand accurately even more challenging. The manufacturing plant doesn’t know what inventory to stock and runs out of certain parts while carrying excess of other unnecessary parts. Dealers are unsure what to stock for customers. Sales teams struggle with knowing – much less presenting – the entirety of available products. Ability to sell becomes compromised.

Extreme SKU proliferation created long customer lead-times and a time-consuming sales quoting process. With thousands of different product choices, salespeople and customers had to contend with endless options, often leading to a pricing war and expensive, custom product builds. Due to a lack of a unified view and insight into their data, the organization suffered the following challenges: A reactive approach to the market due to a disconnect between product management, sales and the supply chain. An incomplete view of customer buying patterns and a lack of, multi-dimensional sales analytics. Data was stored in separate data warehouses and existing tools were not designed to unify or optimize the data accordingly. Guesswork and process inefficiency in sales due to a disconnect between the sales funnel and the ordering system. For example, each unique proposal entailed 38 steps and about 30 minutes to create a single configuration file. The qualitative impacts of all of these factors resulted in lost sales, slow response time, reduced sales efficiency, long lead times, and more. Product variety equated to millions of dollars in wasted productivity. NCR knew that the information contained in their data, such as customer buying patterns, is the life blood of their company, but the question was how to bring it together. NCR needed a way to gain a unified view into their data to more accurately sense and shape demand and apply that intelligence across Sales, Operations, and Solutions Management. They sought a simple way to improve visibility into their product offerings to ensure they were selling the right product to meet customer needs, improve profitability and accelerate commission payments.

Thousands of possible product configurations created complex data that the organization couldn’t untangle, and hence no one had visibility into what customers were buying or how that aligned with the supply and delivery chain. The result was additional complexity and expensive inventory and production challenges, including extended planning cycles, longer lead times, and higher product costs. AGCO was manufacturing high horsepower tractors at the cost of $225,000 to $275,000 per unit, a high number by industry standards. The strong cyclical demand for the units overtaxed production capacity half the year, and the company lacked a reliable method for accurately forecasting that demand. Thirty percent of orders were for retail, customer-specific products and these were accurate, but 70% were dealer projections, based on what sold last year and dealers’ estimates on how demand may change in the coming months. Compounding the problem was the wide range of product variety. The range of option combinations and product adjustments for each market forced a very low ‘repeat rate’ of 1.5 tractors per year, meaning only three of the exact same tractor were manufactured every two years. The product variety created excessive design and coordination costs and the company’s ability to meet market demand was uneven. Finding they had the wrong option combinations in stock, AGCO and their dealers were too often saddled with vehicles customers didn’t want. These had to be discounted to sell. Meanwhile, an order that defied the forecast meant building a single product to satisfy customer demand. What’s more, excess inventory created an expensive problem for dealers who had to store and maintain extra product.

Independent office products dealers were struggling to maintain their competitive edge due to a lack of insight into customer buying patterns. This resulted in lost profit opportunities, primarily because of the proliferation of product-related data across multiple sales channels and the inability to convert customer-buying patterns into cross-selling opportunities. The dealers needed a way to identify and analyze real-time buying patterns across multiple channels to boost sales and regain their competitive advantage.

When introducing new dresses, David’s Bridal faced a significant challenge with a low forecast accuracy rate of 48%. The primary issue was the difficulty in aligning customer demands with product selection, leading to heavy markdowns and slow-moving inventory, which negatively impacted margins. Previously, the company tested samples of new styles in 10 stores before a full rollout, a process that was both expensive and time-consuming, adding approximately three months to the launch timeline. Additionally, this method risked biased results as sales associates tended to push new dresses harder than usual, skewing sales forecasts optimistically.

A Women’s Apparel Company’s Senior Management Team engaged with First Insight to improve their product development and selection processes. The company had a low forecast accuracy rate of 48% when introducing new dresses, leading to heavy markdowns and slow inventory movement, which negatively impacted margins. The in-store testing method was expensive, time-consuming, and potentially biased, as sales associates pushed new dresses harder than usual, skewing sales forecasts. The company needed a faster, more cost-effective way to accurately test new products to select the right inventory that resonated best with customers, achieving higher full-price sales and reducing markdowns.

A Big Box Specialty Retailer entered a strategic relationship with a specific vendor, who had developed an exclusive yoga & fitness wear for Big Box Specialty Retailer and was also seeking to significantly expand its assortment in the women’s running and men’s apparel. The Big Box Specialty Retailer, in partnership with the vendor, engaged First Insight to examine the market potential for the new line as well as identify potentially strong and weak items across the board. The vendor has solid brand equity in footwear, but still struggles to establish a reputation in apparel. The Big Box Specialty Retailer was taking a significant risk in expanding the vendor apparel lines. Retailers in the big box market rarely test items in advance. Buyers typically purchase from CAD drawings and, in apparel, may get to review single samples from the vendor. The vendor was going to support the apparel launch with co-op dollars and adspend. To test the product in time for the campaign, First Insight was able to deliver test results in time for informed key buys. The vision was to reduce the risk for both retailer and vendor in this new program and to allow the Big Box Specialty Retailer to expand its space and inventory investment in the vendor with confidence.

rue21 was in the middle of a turnaround after emerging from bankruptcy and needed to improve business performance quickly. Traditional product testing took six to nine months and was very expensive, with a high percentage of new products failing to meet success criteria. This led to significant delays and gaps in understanding customer preferences. The merchant team was hesitant to integrate new technology into their processes, adding to the challenge.

For over 3 years the Fast Fashion Retailer posted negative comparable store sales and needed to find a way to make smarter assortment buying decisions. Traditional in-store testing methods, which often took weeks if not months to complete, did not work for the company’s ‘fast fashion’ product development timeline. They needed a new process that was faster and provided a data-supported approach to their decision-making process. The Fast Fashion Retailer built and grew their business by introducing the latest trends and designs to the market faster than their competition. As the economy struggled and consumers became focused on value and more selective in their apparel spend, the Fast Fashion Retailer began to lose market share. It was imperative for them to find a way to maintain their ‘fast fashion’ product development calendar, while incorporating more consumer insight data into their decision-making to better resonate with current and lapsed customers. Traditional in-store testing methods took too long to receive results and did not fit into the Fast Fashion Retailer’s nimble product development cycle. Merchants and designers were left to make large investments with minimal direct-from-consumer data. This inability to align product development with consumer demand left the Fast Fashion Retailer with sub-optimal sales and excess inventory.

Developing new products in the fashion and footwear industry is fraught with risk, as brands may invest significant resources only to find that their products do not meet customer expectations. This challenge is exacerbated by the rapid changes and trend influences in the industry. Crocs, known for its comfort-focused clog shoes, faced a decline in relevance due to a lack of style and innovation. Despite efforts to bring new designs to market quickly, Crocs struggled to consistently identify which categories represented the best opportunities for new products or line extensions. The company needed a more effective way to validate and test designs to ensure they resonated with customers and aligned with market trends.

The Integrated Shoe Retailer was losing market share in many of its highly competitive channels. They were having difficulty aligning evolving consumer style requirements with product design elements. For some of their nameplates, this limited their ability to sell into new retail channels or expand market share in existing channels causing downward pressure on margins. The Integrated Shoe Retailers’ current in-store testing method was expensive and largely dependent on sales-only feedback in a limited store setting. The company did not do any consumer testing for some nameplates. Where consumer testing was done, it was inconsistent due to the nature of testing in different stores, different ways. The company wanted to select the “right” assortment that resonated best with specific customers, enabling the Integrated Shoe Retailer to achieve higher channel penetration. They aimed to identify design elements that were saleable to customers identified with a specific channel because they understood that not all channel customers were the same.

Vera Bradley, a fashion accessory and handbag retailer, faces the challenge of accurately predicting consumer interest and optimal pricing for new products. The company constantly introduces new products, making educated guesses on customer interest and pricing. Pricing too low risks losing margin, while pricing too high may result in unsold items. The uncertainty in consumer preferences, especially in the fashion industry, adds to the complexity. The goal is to offer the right product assortment at the right time, price, and place to optimize sales and product performance.

Dick’s Sporting Goods faced the challenge of differentiating its product offerings to attract consumers away from competitors. The retailer aimed to make both its private labels and branded merchandise appealing to shoppers. However, the industry average for new product success rate is below 40%, making it difficult for Dick’s to build a selection that would stand out. This challenge was exacerbated by Amazon’s growing product range and dominance in the sporting goods market, with Amazon launching its own activewear private labels and achieving significant sales growth in sports and outdoor categories.

A brand/retailer was concerned about the impact of tariffs on their margins. They wanted to understand in which retail channels they could increase prices on specific programs/items without receiving price resistance from customers. The challenge was to identify which items could bear the price increase and which could not, in order to optimize their pricing strategy and maintain profitability.

In today’s competitive hiring environment, identifying and attracting the most qualified candidates is challenging even for top tech companies. Facebook has faced difficulties in finding data scientists with the right expertise and skills. Traditional hiring methods, such as resumes and interviews, often fall short in revealing the true capabilities of candidates. To address this, Facebook began running Kaggle competitions in 2012 as part of its data science recruiting strategy. These competitions are designed to attract a diverse pool of data scientists and test their skills in real-world scenarios.

Many companies are cautious about releasing data online due to customer privacy and competitive industry concerns. This is particularly true for industries dealing with sensitive information, such as health insurance. Deloitte Australia faced this challenge when they wanted to offer expanded analytic services to their client, HCF, a health insurance provider. The sensitive nature of health claims data made privacy an ongoing concern, even though the data was anonymized. Deloitte needed a way to leverage advanced data analytics without compromising data privacy or intellectual property (IP).