Four Ways Executives Leverage Sales Data to Drive Revenue and Increase Efficiency

May 15, 2017

Topic: Corporate, Customer Experience, Market Analysis, Product Lifecycle
Resource type: Insights Blog

In a recent study by Teradata, 87 percent of marketing executives said data was their organization’s most underutilized asset. While many executives see the value in developing insights using internal and external data, it can be difficult – and time-consuming – to make sense of it all. As a result, many businesses are data-rich but information poor.

Considering this challenge, Hanover’s quantitative research team outlines four distinct approaches businesses can take to leverage their existing data to drive revenue, improve profitability, and reduce operational complexity.

Approach 1: Product Analytics

What Is It?

Product analytics can help executives identify a) what offerings or services could be discontinued with minimal impact to their business, and b) what offerings have considerable growth potential. This type of analysis weighs variables that are most critical to an organization – including revenue, profit margin, and customer impact – to drive decisions that both streamline product offerings and enhance profitability.

How Would I Do This?

Taking a multi-dimensional approach, businesses can use their data to calculate an “importance score” for each of their products. This score predicts the potential impact of cutting a product, based on the variables deemed most critical to the business. This type of analysis lends itself well to interactive visualizations, which allow executives to observe the effects of each of the variables and manipulate data in real-time to guide strategic planning discussions.

What Are the Benefits?

This approach is valuable for businesses looking to:

Reduce costs by discontinuing products that do not expand market share
Save on product development costs by focusing on products that will expand market share

Approach 2: Forecasting Analytics

What Is It?

Businesses with diverse product portfolios often face challenges in tracking clear sales patterns. To combat this difficulty, executives can use data on purchasing trends over time to determine which products have related sales patterns and which operate more independently from one another.

This approach enables forecasting at the product-level, by correlating products that have similar patterns. It also allows businesses to predict sales for each of the unique groups identified through the analysis.

How Would I Do This?

Leveraging sales data across a set timeframe – typically monthly – businesses can calculate purchasing trends over time for each of their product offerings and create unique groupings based on these correlations. These groupings can then be forecasted on a monthly interval to inform how much of each product should be manufactured to meet future demand.

What Are the Benefits?

This approach is valuable for businesses looking to:

Ensure efficiency gains by making sure units are not drastically over- or under-produced in the coming business cycle

Approach 3: Customer Analytics

What Is It?

Identifying the descriptive characteristics of customers and creating segmentations based on certain behaviors—whether those are products purchased, times clicked on marketing campaigns, or other observable behaviors—helps businesses keep a pulse on who their most valuable customers are.

A better understanding of these top clients, what products customers are purchasing, and what specific segments of customers exist aid a company’s strategic marketing and planning efforts. Customer analytics allow executives to tailor marketing campaigns to specific audiences, as well as reach customer segments that might be underperforming.

How Would I Do This?

Drawing on variables of interest to executives, businesses can perform a cluster analysis to segment customers into similar groups for marketing, planning, and targeting purposes. Cluster analysis groups customers together based on their similarity of a range of variables. The first step is to determine the number of clusters that is most appropriate to describe the data – the appropriate number will vary, and depends on the amount of variation in the dataset. The goal is to minimize the number of clusters to ensure the results are interpretable and actionable.

Businesses can also leverage their data using less complex analysis types. A descriptive analysis of customer data, for instance, can aggregate selected variables from a sales dataset to help executives identify purchasing trends and values for each of its customers.

What Are the Benefits?

This approach is valuable for businesses looking to:

Identify customers and products/services associated with higher sales, and subsequently focus sales efforts on these individuals
Eliminate product/service characteristics that negatively impact sales

Approach 4: Pricing Analytics

What Is It?

How sensitive are your customers to pricing changes? For businesses hoping to optimize the price for specific offerings, understanding the relationship between price and quantity sold is critical. With sufficient data, a price elasticity study allows executives better understand this relationship by measuring the volume of sales for the same product at varying price levels.

How Would I Do This?

This type of analysis requires a considerable amount of historical sales data—around five years as a baseline for the offering of interest. Pricing elasticity is a data analysis approach using pricing and purchasing data to evaluate the impact of a marginal increase in price on the number of customers that would purchase at the price. The optimal price point is identified when the maximum amount of revenue has been obtained.