Data Analysis In the last two years, more data was created than ever before. Companies who analyze their data effectively enjoy an average of 8% increase in revenues and a 10% reduction in costs – but just under 1% of the data collected is ever analyzed in the first place. Why? Many companies are simply overwhelmed by the sheer amount of data they have on hand and are at a loss on where to begin their analysis.

The culprit for this phenomenon has to do with how companies approach data analysis in the first place. Many companies begin the process by identifying the data they have available and then sifting through it to see if there is any actionable insight to be gained. With no goal or strategy in mind, companies quickly become daunted by the amount of time and resources necessary to parse through all of that information. Instead, the key to successfully extracting insights from data is to begin with the business challenges or goals. Reviewing existing company data through the lens of solving a specific business challenge makes sifting through data more manageable. Moreover, by leading with the business goal, companies are better prepared to not only leverage their existing data, but they can also better determine what new data might be necessary to collect. Below, we’ve outlined five common business goals and detail ways companies can leverage existing data and collect new data to drive insights that connect to business advancement.

Goal 1: Improve customer targeting

Leverage Existing Data: Most companies sell to a wide variety of customers who have different motivations for buying the product. Understanding which customer groups will respond more favorably to different products and messaging is key to inflecting sales and customer retention.

A Customer Segmentation Analysis leverages customer usage data that many companies already have on hand to identify groups of customers that are meaningfully similar across multiple factors including demographic, behavioral, and psychographic characteristics.

Collect New Data: Customer Segmentation Analysis can also be augmented with new data collected by surveys of current and prospective customers. The surveys could collect information on purchase motivators, brand preference, and product feature requirements. Companies can use these segments to improve targeted marketing, inform product development and messaging, and determine inventory levels.

Goal 2: Bring a successful product or service to market

Leverage Existing Data: Many companies already collect customer purchase data and perform competitive scans of other companies’ offerings. In addition, manufacturing data can give companies a sense of how long it takes to create certain product types or parts, informing decisions about the efficiency of creating a new product in the existing production environment. By leveraging these three sources, companies can make data-driven decisions around which products will likely be most profitable within their current production capabilities.

Collect New Data: Roughly 95% of new products fail because they do not properly address customers’ needs and wants. To get a more thorough understanding of customers’ expectations for products, a Conjoint Analysis measures the value of various features of a product or service as the users are asked to rank order different product features. This technique is based on the idea that product or service features can be measured more accurately together rather than individually. This practice is especially effective for multi-product lines, giving companies the tools to bring the right combination of products and features to market at the optimal price point for their customers.

Goal 3: Increase sales at the point of purchase

Leverage Existing Data: Customer purchase data is a readily available source for companies to increase sales. This data can correlate which products sell more frequently at specific stores, and highlights seasonal correlation to the purchases. Leveraging purchase data to ensure stores and warehouses are stocked according to purchase trends can help increase sales and decrease order-to-delivery times.

Collect New Data: In an increasingly competitive marketplace, companies often miss valuable opportunities to intervene at key moments in the sales cycle. Consumer Decision Trees enable companies to step in when sales are at stake by identifying the critical junctures where consumers choose to make or abandon purchases. Gathering the data through focus groups or surveys, these analyses break down every point of the customer journey, highlighting how each decision impacts the likelihood of a purchase. In retail, it is especially important to understand how a customer moves through a store or navigates the online shopping process. For B2B companies, Decision Trees can help companies decode the decision-making processes where there are multiple stakeholders involved throughout a long sales cycle.

Goal 4: Increase customer satisfaction and retention

Leverage Existing Data: It costs 6 to 7 times more to attract a new customer that it costs to retain an existing customer, making customer satisfaction and retention a priority among many companies. Optimizing customer retention rates and service data can be an effective way to increase customer satisfaction. Companies can analyze the number of customer complaints and number of repeat service calls, as well as a social media sentiment analysis to see what is driving customer dissatisfaction and customer loss. This is an effective way to prioritize problem areas, driving up satisfaction rates in the process.

Collect New Data: The Net Promoter Score® (NPS®) Analysis measures a customer’s likelihood to recommend your company, product, or service to others and is widely seen as a common indicator of customer satisfaction. While simple, the NPS analysis can provide a useful checkpoint on customer satisfaction and loyalty that companies can benchmark over time.

Goal 5: Maximize store or distribution center location

Leverage Existing Data: Companies can tap into the Internet of Things (IoT) to create unique opportunities to act on information they already gather. Beyond customer location data, many companies already gather supply chain information through data-driven intelligent systems. Smart machines throughout the supply chain allow for increased effectiveness through shop floor operational improvement, plant load optimization, rapid costing, and health, safety and environment tracking. Weighing the importance of each of these factors, as well as benchmarking delivery and service times, gives companies the tools to prioritize certain locations for a new store or distribution center.

Collect New Data: Companies can maximize sales with increased foot traffic in a new store location, or minimize delivery costs with the optimally placed distribution center. To ensure companies choose the best geographic locations, a Spatial Analysis combines customer and publicly available data. Focusing on the demographics of the proposed locations, access to roads, public transportation and other facilities gives detailed insight into the optimal location for the new location. This simple analysis can inform decisions to maximize store traffic or minimize distribution costs.

Data Analysis: Five Ways to Get the Most Out of Your Data

Hanover Research