At Quisitive, we subscribe to the philosophy that data is an essential asset of any business. Unfortunately, data sometimes gets a bad rap, which is perhaps understandable. Many businesses become reliant on dated technologies to store and organize expanses of historical data. They also struggle to manage and consolidate multiple data sources and often green-light indiscriminate data projects that, instead of reaping insights, simply leave teams and leaders frustrated.

These data challenges are not uncommon. One of our recommendations centered on the notion that leaders must focus their efforts on the questions with the greatest potential to impact the business positively. While this may seem intuitive, focusing on these questions warrants further expansion on business goals that drive value from data. However, we’ve found that many business leaders couldn’t articulate what exactly they were hoping to accomplish once they made the move to an as-a-Service cloud-based model.

In a basic sense, leaders are typically seeking a single view of their business or desire a single view of their customers (what is often referred to as a 360-degree view of the customer). This, however, is insufficient in understanding the actions that will be taken to accomplish core business objectives. Through our experience in working with clients over the past 16 years, we have found that most data projects are launched to achieve one of seven business goals. By identifying business objectives at the onset of the project, we can more clearly estimate the opportunity, define success, and develop a comprehensive data strategy and analytics roadmap. Thus, the seven business goals for deriving value from data are described below.

1. Improve Customer Acquisition

This customer-centric goal allows organizations to develop a more targeted marketing spend. Business leaders hope to increase gains by either improving conversion rates or increasing revenue with reduced marketing spend. Rather than using a “blanket approach” or a “one size fits all” method to marketing and customer messaging, companies are seeking to use their data to better target and segment their customer base, predict who is most likely to convert, target those with the highest Customer Lifetime Value, and personalize promotions through timely and relevant messaging. Therefore, if you are looking to improve customer acquisition, data should be used for:

• Customer Segmentation: Grouping customers together by similar profiles or behaviors
• Lead Scoring: Evaluating target customers’ chances for conversion
• Customer Lifetime Value: Predicting total anticipated revenue generated from each customer throughout the customer journey
• Personalization: Targeting prospects with content/offers that increase chances of conversion
• Funnel Analyses: Mapping the customer’s buying journey to determine what motivated the sale or uncover where the sale was lost

2. Generate More Revenue from Existing Customers

A second customer-centric objective is to increase revenue from your existing customer base by expanding exposure to other product offerings. Once you get them, how do you get more money from them?  This typically involves some level of personalization, such as recommending specific products for individual customers based on past purchase history or current content-browsing. Recommended product algorithms are the cornerstone for upselling, down-selling, and cross-selling, keeping in mind that there are times when down-selling is more profitable than upselling or cross-selling (e.g., generic brands, used products, or any other situation where less expensive products/services have a lower cost of acquisition/implementation. Ultimately, this business goal is about using data to identify customer characteristics and behaviors to better anticipate their needs. Therefore, if you are searching for ways to generate more revenue from your existing customer base, data should be used for:

• Personalization: Enhanced recommendations based on knowledge of a customer’s purchasing/browsing behaviors
• Upselling, Down-selling and Cross-Selling: Most often accomplished via recommended product/service algorithms (either through “real-time” offers or batch follow up campaigns),
• A/B Testing: Experimental testing of marketing and sales activities and/or design choices
• Pricing Analysis: Predicting pricing sensitivities for dynamic pricing models

3. Retain Customers

A third customer-related goal anticipates losses to a customer base by providing evidence of who is likely to leave, what the common causes are, and how a company should best intervene. Intervention strategies, such as personalized messaging linking the potential cause to a likely future behavior, promote a better experience, and encourage customer retention. Focusing on this goal permits companies to provide the right resources to the right customer. Keep in mind that some customers are not worth saving; that is, they can be a resource drain which ultimately costs you more to maintain than their Customer Lifetime Value would dictate (though we must be careful how we drive customers away in a world of expansive social media reach).  If you are trying to stop the bleeding, data should be used for:

• Customer Churn: Predicting when customers will leave before they leave
• Personalization: Connecting customer segmentation and agent data to target the customer service experience and personalize the intervention
• Customer Lifetime Value: Understanding which customers are worth saving based on their anticipated total lifetime value
• Social Media Analytics: Using text analysis to understand brand awareness, reveal customer sentiments, and enhance customer engagement

4. Reduce Operating Costs

Reducing operating costs should be a goal of every company to increase the bottom line and ensure that both effort and funding are expended appropriately. Companies that operate in the status quo without analyzing data for potential efficiencies and optimizations are unable to grow. Eventually, competitors will catch up and find ways of offering products and services faster, with lower pricing. Data is critical for evaluating where the company is hemorrhaging undue resources and generating a plan for optimizing production potential—including workforce, machinery, and logistics. Data can identify patterns of costly operating procedures and outcomes—such as customer fraud, inefficient distribution, or employee turnover—that cost the business money. If you are evaluating ways to reduce operating costs, data should be used for:

• Analytics Dashboards: Providing “real-time” insights into operations and costs by creating a single view of the business by incorporating multiple sources of operational and financial data
• Voluntary Employee Turnover: Predicting which employees will leave before they leave
• Workforce Management: Optimizing level of experience and team structure for maximum return on investment
• Preventive Maintenance: Using sensor data to anticipate equipment failure
• Production Optimization: Maximizing production while balancing operating costs
• Supply Chain Optimization: Recommending logistics based on anticipated demand and potential downstream bottlenecks
• Fraud Detection: Identifying anomalous patterns of behavior as indicators of costly fraudulent activity

5. Calculate Risk

A fifth goal of organizations involves assessing risk. Risk assessments are useful when companies need to prescribe actions based on calculated risk and reward scenarios. Critical for planning and increased predictability, sophisticated algorithms dictate when an action should be chosen over another, and what stands to be gained or lost as a result of that decision. Data science techniques centered on root-cause analysis are especially helpful in understanding the types of failures, frequency of failures, effects of failures, and potential severity of effects. Some common applications where data is leveraged to assess risk include:

• Actuarial Science: Identifying predominant risk factors and calculating associated risks, especially common in insurance, loans, and finance recommendations
• Litigation Risk: Assessing likelihood of a costly lawsuit associated with company’s operational procedures
• Health Risk: Potential complications associated with human behaviors or health procedures
• Go/No Go: Any prescriptive operational decision made by risk assessment

6. Improve Forecasting

The sixth business goal focuses on predicting future outcomes for better planning. This often includes having a clear understanding of which products or services are in demand and how a company can be ready to capitalize on that demand when the opportunity surfaces. Improved forecasting implies that companies are no longer leaving money on the table because they weren’t properly prepared, such as having the appropriate inventory in the preferred location at the relevant time. Ultimately, increased visibility into upcoming sales cycles equals better planning for a company’s future. To improve business forecasting, data should be used for:

• Sales Forecasting: Predicting sales for any period of time
• Demand Forecasting: Predicting demand for a product or service over a designated time period
• Inventory Management: Adjusting inventory levels based on anticipated demand and other market factors
• Pricing Analysis: Dynamically adjusting pricing based on anticipated availability of a product or service to maximize profitability

7. Create an Entirely New Revenue Stream

Finally, an organization may want to capitalize on its data by developing an entirely new revenue stream. This is especially common in B2B companies where businesses can develop products or offer services centered on creating insights for their customers. That being said, more and more B2C companies are realizing the potential of their data for deriving insights that customers are willing to pay a pay a premium for (e.g., premium memberships/subscriptions providing access to customers’ own usage data). Every business should look for opportunities to create new revenue streams and potentially invent a new market for which there is no competition. To best accomplish this goal, companies should consider:

• Product Development: Developing enterprise-level analytics products
• New Service Offerings: Capitalizing on data to provide additional services back to customers
• Application Creation: Creating apps to automate processes and facilitate data collection and analysis

Today, business leaders are using data to acquire more customers, generate more money from their existing customers, retain customers, reduce operating costs, calculate risk, improve forecasting, and create an entirely new revenue stream. When the proper business goals are aligned with a solid data strategy and the proper technologies, business leaders quickly learn that data can drive true transformation, which can, in turn, amount to significant revenue generation and cost savings.