All posts by Cherise Fiorante

3 Major Concerns for Revenue Forecasting

Revenue Forecasting presents a major challenge for companies, especially in finance and marketing. Erroneous forecasts can be disastrous for a companies bottom line, especially when money is spent in anticipation of higher revenues. While the challenges are numerous, there are some actions a department can take to ensure their forecasts are accurate and continue to be accurate.

1. Bad Data

This won’t come as a surprise to many. Every company has bad data, but does this mean you can’t get an accurate forecast, no. First, the data should be analyzed for the causes of errors. Once the causes are known, the errors can either be removed or accounted for, in a model, greatly enhancing the accuracy.

Not all your data, therefore, is bad. The trick is to identify the data that is good, which will act as a core baseline. This baseline can then be used to identify variations and fluctuations to determine the cause. Factors, such as spikes in class center volume, vacations by salespeople, or temperature all can impact results.

Our data scientists, continually find ways to mitigate the issues of bad data through statistical imputation and simulations. These methods can greatly enhance an understanding of the effect of the bad data, as well as provide methods to remediate the issues.

2. Changing Environment

“Change is the only constant in life”, was a famous quote by Heraclitus. Many revenue forecasting models preach “teach it and leave it”, as though it can continue to provide accurate forecasting without ever modifying it. However, this isn’t really the case. Economic environments change, organizations change strategies, and customers change preferences. In some cases, models can pick up the changes, but more complex models require retraining of the model. While many companies say this can occur automatically, it cannot.

Take for example a scenario in which a variable was never included in the original model. If this variable becomes important at some point in the future, the model could never compute its impact, yielding an incorrect result, such as would occur with a new product category or removal of an entire product category.

We think of this as driving a car, always making adjustments. While generally a smooth ride, there are times in which scenarios appear, where traditional training doesn’t work and we need to make a split-second decision. Algorithms cant generally do this, without massive computing power, such as what is needed for self-automated driving.

Change is the only constant in life – Heraclitus

Revenue forecast models need regular and periodic review for accuracy and training.

3. Bad Interpretation

Ultimately, the models produce a number for the forecast, models that are too general have increased errors overall, while specific models have fewer errors for training sets, and increased errors for data it hasn’t seen. Analysts’ interpretations are critical when this occurs since the end-user of the forecasting model isn’t fully aware of what events are causing fluctuations.

One way forecasters combat this is to create an ensemble model, which aggregates a number of models together. The challenge with this approach is that many there may not be an insight into which part of the ensemble is more weighted, thus the ensemble simply tries to smooth out these fluctuations, causing a loss of, most likely valuable information.

Creating multiple models allows for a more granular interpretation of the forecast as well as greater insight into the error. This examination of error is critical to understanding the events that are impacting the revenue. Simultaneously, it creates a better-disciplined process by which analysts, forecasters, and end-users are diligent in understanding as much of the variation as possible.

Creating forecasting models in a vacuum should be a serious concern for the organization. This black-box approach, without truly understanding the effect of multiple data points and methods can lead to a forecast accurate in some instances, but which can lead to failures down the road. We believe these black-box approaches create more problems, and that a solid repeatable reviewable process is the most reliable approach and yields more opportunities.

Exploring The Flaws in Earned Run Average

Professional baseball teams have long used metrics  to measure player performance. The Earned Run Average (ERA) has been at the cornerstone of baseball metrics, since its inception in 1912.  Traditionalists have abhorred the use of new metrics conceived by Sabrematricians, however, to many they have proven quite valuable.

In a recent paper to be published and presented at the Northeast Decision Sciences Institute Conference in Providence, RI in March,  the team from 5 Element Analytics scrutinized the ERA statistic to help formulate a better approach to identifying pitching performances that would be candidates for exclusion, otherwise known in the statistical world as outliers.

There is a great deal of insight in this research that exposes the impact of these hidden outliers, which can really create a true measure of a pitchers performance-

Tyler Levine, Pitcher for The Long Island Ducks Professional Baseball Club

The new approach discusses how outliers can be identified using statistical methods by comparing the change in variance of a set of observations by the removal  of a single observation. In this way, a single bad game, or even an unusually good game are identified thus allowing for a true measure of performance. The data was initially tested on the top 10 pitchers in the National League, and identified fewer outliers than other methods, but found a better representation of a pitcher’s “True” ERA.

Since earned runs follow a count process, identifying outliers have benefits in a number of different areas.  The approach could have significant implications for use in other areas such as healthcare, and transportation.

We would like to thank Tyler Levine and Long Island Baseball, in Bellmore, NY for their support during this research.

Click Here for a copy of the paper , “Outier Identification of Count Data Using Variance Difference”, being presented in Providence, RI, in April 2018 .

Should you like more information contact

Kiyra Artisse
Chief Operating Officer
5 Element Analytics
Phone: 516 945 0923
Email: press@5eanalytics.com

Top 3 Reasons Why Analytics Projects Fail

Analytics projects can be very complex and require an appropriate level of expertise. The various stages of an analytics project incude determination of goals, collection of data, cleansing of data, statistical analysis, and presentation of findings. According to Gartner over half of the analytics projects fail, which is determined as a project that fails to meet its stated objects and/or runs over budget or over the stated time.

We have identified the top three reasons why analytics projects fail and it might not be what you think.

1) Lack of a Senior Sponsor

Projects lacking sponsorship of senior leadership have the highest likelihood of failure since they aren’t given the requisite attention of other projects. Disconnected leadership fails to identify or recognize the valuable insights provided by well conducted analytics projects. Further, this dissonance resonates to analysts and data scientists who may not feel their work is valued or may not feel their efforts will be realized and thereby limit their efforts and scope of results. Senior sponsors should be C-level or high level decision makers in the strategic business units, and it is incumbent upon analytics leaders to obtain the necessary sponsorship and support for their teams.

2) Lack of a clear question to be answered

Its the age old question of what are we doing and why for starters. Analytics projects require the inquisitive nature of business units to fuel the analysis. Understanding the nature of the data, and having a clear objective, solidifies the work effort and creates a less amorphous outcome. The question should be business oriented and ask for answers critical to the business such as “Who are our most valuable customers?”, “Which customers are more likely to purchase the higher level product”, and “What happens if I increase/decrease my price and how will it affect long term customer retention”. When these types of questions are asked, analytics becomes a tool empowering business leaders.

3) Lack of reasonable action

“Action is the foundational key to all success”-  Pablo Picasso

Your business units and senior leadership must be willing to take clear and decisive action based on the results provided by analytics projects. Failure to do so results in missed opportunities and complacency. “Let’s increase our price and analyze the results.”, “Let’s focus our latest marketing effort on the highest value customers”, and “Increase our communication on customers who may be ready to quit based on the results” are clear actions from results. The absence of these actions or indecisiveness becomes a clear indicator of a lack of trust in the data, analysis or analysts, and leads to failure.

Action should follow the questions from senior leadership, who are willing to trust the data and analysis as a compliment to the institutional knowledge of the team entrusted to execute on the directions. Analytics projects provide insights, they are not the end but rather a means to an end requiring buy-in and trust at all levels.