Leverage business analytics for greater margin intelligence
Business analytics help you invest in the right products, target the right customers, and optimize your organization. Gain new insight into operations and profitability to improve your cost system and reach your business goals. We lay out the journey.
Most organizations are data-rich and information-poor when it comes to margin intelligence. They’re collecting more data than ever before but often aren’t sure what they should be measuring—what data to extract — and how to leverage it to improve decision-making. The lack of clear visibility into costs and profitability is a source of real frustration, but with the right skills, technology, and approach, you can gain the insights needed to help you invest in the right products, target the right customers, and optimize your operations.
Variances between expectations and actual financial performance, margin erosion, and significant changes in operations or product mix all indicate your costing system needs to provide better intel. The good news is, quick wins are possible, and each stage of the analytics continuum unlocks new insights. Let us explain.
Lay the foundation for analytics: Overcome data roadblocks
Business analytics initiatives are dead in the water without good input. Missing data, duplicated and disparate data, inconsistent definitions, manually intensive data management processes, and “information brokers” holding data hostage (e.g. Joe in Accounting is the only person that has that information”) are all common issues companies face when starting out on their analytics journey. Sound familiar?
Cleaning up the data and setting up centralized data repositories to provide a single source of truth and standard definition of your information is the first step. Very few business intelligence programs are successfully implemented with enterprise-wide data from the start, although that’s always a long-term goal. To get there, select discrete, targeted focus areas and begin iteratively building airtight data acquisition, data management, and data governance processes.
Seek early wins with descriptive analytics
Greater margin intelligence through business analytics occurs along a continuum, from hindsight to foresight. Descriptive analytics represent the early “wins” of the strong data foundation you’ve laid. Descriptive analytics show you, at a high level and in real time, what’s happening:
- Am I hitting my targets?
- Which customers have declining revenues?
- What’s my sales order backlog?
- Which SKUs have the lowest margins?
- What’s my daily revenue? Daily liquidity? Daily sales? Monthly costs?
Our clients are often surprised how much they learn at this stage. We recently worked with a business using a legacy ERP system. After cleaning the data, we built out dashboards using a popular business intelligence (BI) tool, providing leadership with timely access and insight into never-before available cost and revenue trend data by part, by customer, and by program.
Answer “why?” with diagnostic analytics
Diagnostic analytics capabilities help clients answer, “why did this happen?” Strong data skills and tools (e.g. data discovery, data mining, and correlation analysis), in conjunction with industry experience and tribal business knowledge, can begin to shed light on particular questions and challenges such as:
- We increased prices in a particular market; why didn’t we reach our target margin?
- Why did labor costs go up without any changes in shifts or increase in total hours?
- Profitability dropped for some customers; was it due to cost increases or revenue declines, or both?
The value of diagnostic analytics lies in the ability to identify and understand patterns, anomalies, and root causes. These proverbial “needles in the haystack” aren’t often visible to the naked eye, but analytics technologies enable the recognition of data trends and relationships that were previously unrecognizable.
Assess outcomes with predictive analytics
The ability to predict the probability of future outcomes is the real motivation and value in the aforementioned foundational investments in analytics. The ability to use data to develop more accurate plans, to be more effective with the deployment of resources, and to reduce the risk of key business decisions is after all the end goal. In order to get there, predictive analytics requires much more sophisticated data models and the application of advanced statistics, but these capabilities are becoming more available to the mainstream.
Predictive analytics helps answer questions such as:
- If a commodity price changes, how will it impact my product margins?
- How likely are certain customers to stop purchasing products if I raise the price?
- Which of my product launches are likely to be the most successful?
Consider these actual applied scenarios:
A manufacturer levies a price increase and unexpectedly loses several key customers. The company needs to further improve margins, and the CFO is considering another increase, but can’t afford another significant decrease in sales volumes. What if the business could strategically target the price change on a customer-specific basis? Using predictive analytics tools and techniques, such as logistic regression, the business can model out the likelihood of specific customers leaving as a result of an additional price increase and accordingly tailor their strategy to optimize revenue while mitigating risk.
A service company wants to know when to replace its fleet of heavy-duty trucks. By taking data from multiple systems, cleaning it up, and creating a data warehouse, we help such businesses drill down into their ownership and operating costs and proactively plan the optimal replacement mileage.
A public hospital wants to better understand its emergency room (ER) population trends. Using analytics, they’re able to proactively identify patient populations with frequent visits to the ER that could be treated more effectively in nonurgent care settings. Process improvements informed by this data result in lower cost and increased continuity of care for the patients as well as reduced cost of care for the hospital.
A clothing design business relies on successful anticipation of customer preference and demand to align production planning with anticipated sales. Applying predictive analytics to historical sales data, CPG companies can more accurately predict new product sales trends and optimize decisions around dedicated production lines and optimizing purchasing decisions.
Beware the caveats
Amid unlimited possibilities, we offer two caveats:
Don’t try to make the leap to predictive analytics too fast. Without a solid grounding — clean, centralized and governed data and a clear grasp of what is happening, why it’s happening, and what can be safely interpreted as a causal relationship — it is too easy to jump to (wrong) conclusions based on what you want to see in the data, versus what it’s really telling you.
Conversely, at all stages of the analytics maturity continuum, it’s crucial to evaluate the outcomes of analytical systems and processes through the prism of your business experience and knowledge. Successful initiatives require building capabilities and “muscle” in both the data management, analysis and visualization, as well as the business and subject matter expertise to appropriately apply that information and insight to actionable business decisions in the appropriate manner to meet your specific business objectives.
Take a pragmatic approach to analytics
You might be surprised how quickly you can extract more value from your data. Working with an experienced analytics consultancy, you can get started quickly with the right tools and methods for the job. Successful analytics programs start with curiosity, are built iteratively, and build on the momentum created by end-users experiencing a series of wins.
Begin with the end in mind: what are the key business questions you want to answer and issues you want to resolve? Pick a specific area of the business to start. Where can you have some quick wins? Issues concerning materials and scrap, backorders, or inconsistent or off-target production volumes can all deliver high-value impacts — but more essential is that you begin anywhere you have a curiosity and the data to satisfy it.
Build your program on a foundation of solid data. Identify and map your key data sources (and missing data), centralize and integrate your data, and identify and address data quality issues. Facilitate a collaborative environment between various stakeholders (e.g. IT, operations, and finance). These multiple perspectives are necessary to identify what data is critical, validate the data available, determine how to obtain missing data, and ultimately to create actionable information driving more effective, profitable decision-making.
Start small, but start now. Expand your efforts iteratively, like a trail of breadcrumbs … one answer leads to the next question. The sooner you start, the sooner you’ll begin turning your data into information, identifying opportunities previously unrecognizable to you, and making confident decisions to increase the efficiency of your operations, maintain your competitive advantage, and improve overall profitability.