Do you want to analyze and determine your profit margin? If YES, here is detailed guide on how to use a time-series analysis to calculate your profit margin. Business forecasting over the years has been crucial to every organization’s capability for both strategic and tactical business planning. With the aid of a detailed time series analysis, data can be taken on a daily, weekly and monthly basis to create predictive analytics.
To effectively predict and shape profit (Predictive Analytics) for a company is a very important business tool in this age. The marketing team of an innovative business will always want to build a prediction model for profitability in a new location, or profit margin for a new product. The time series analysis and data mining techniques can be combined to do predictive analytics from large amounts of data.
This predictive analytics predict the present profit focus by analyzing what happens within a stable and current situation, based on a set of assumptions that describe reality. Then evaluate and predict the profits of the remaining years.
Table of Content
- Why Time-Series Analysis is the Best Tool
- 1. Define Business Objectives
- 2. Preparing Data
- 3. Sampling Your Data
- 4. Building the Model
- 5. Deploying the Model
- Challenges of Using Time – Series Analysis in Preparing Predictive Analytics
Why Time-Series Analysis is the Best Tool
In the United States, data analyses have been a growing importance especially on the stock market. In order to get the profit of the investment, many investors strive to understand how to analyze data from the stock market.
Aside from business management and growing profits, time series analysis is the best way to acquire and analyze these data. For instance, experts note that acquiring a new customer is seven times more costly than retaining one. Being able to minimize customer churn helps businesses reduce costs as well as build a larger loyal customer base.
Accurately noting dissatisfied customers and personalizing offerings for them is one of the most common situations in which time series analysis and predictive analytics is applied. To calculate the “likelihood to churn” score, a business first uses the time series analysis to collect data on customer profiles, transactions, and feedback.
This data is then input into predictive analytics tools that use techniques such as correlation analysis and multiple regressions to identify customers that are likely to churn. Based on the churn score results, a business can then prepare personalized promotion offers such as discounts, exclusive memberships, and other special concessions to woo these customers back into the fold.
In addition, setting the right price for a product or service can be tricky. A higher price may deter consumers and reduce sales volumes, while a lower price will result in thinner margins. Time series analysis, coupled with predictive analytics, can help a business arrive at the best price at which to sell your goods and services.
Predicting consumer demand for a products, as well as understanding customer behaviour, buying patterns, and market trends will help a business optimize prices and improve profit margins and inventory management.
How to Use a Time – Series Analysis in Predicting Business Profit Margin
You must create a data – driven culture within your business to ensure you are generating the type of data you need to get predictive analytics right. And a sure way to create and generate accurate data is through the time series analysis. Below are steps to guide you as you prepare your business for predictive analytics, which in this case is to predict present and future profit margins.
1. Define Business Objectives
This process starts with using a well – defined business objective. A well-prepared predictive analytics is supposed to address a business question. Clearly stating that objective will allow you to define the scope of your project, and will provide you with the exact test to measure its success. Clearly defined objectives help to tailor the predictive analytics solutions to give the best results. Some examples of business questions to which this analysis can provide answers are:
- Which of my customers/customer segments are likely to remain loyal without any incentives?
- Which product will most likely be in demand during the end-of-year sale?
- Which of my B2B customers is likely to default on payments?
- Which of my suppliers will likely not deliver raw materials on time?
- Which areas of production might see an increase in costs in the coming quarter?
You may discover that your existing data is not sufficient to answer your questions. In these cases, you will have to use the time series analysis model to collect adequate and correct data
2. Preparing Data
You’ll use historical data gotten through time series analysis to train your model. The data is usually scattered across multiple sources and may require cleansing and preparation. Data may contain duplicate records and outliers; depending on the analysis and the business objective, you decide whether to keep or remove them.
Predictive analytics models are fed by data. Therefore, identifying the right data that can answer your business questions is important. If you store your data in spreadsheets, pulling them into your predictive models can get tedious and may not even be possible in all cases.
Instead, use your CRM applications, point of sale software, marketing tools, and other software to store relevant data. These tools allow you to store larger amounts of data (often in the cloud, helping you save IT infrastructure costs) in a neat fashion. You can then use data extraction tools to pull data from multiple sources. APIs also allow you to connect multiple apps to collect data.
3. Sampling Your Data
Data gotten through time series analysis and intended for predictive analytics needs to be split into two sets: training and test datasets. You build the model using the training dataset. You use the test data set to verify the accuracy of the model’s output. Doing so is absolutely very important.
Otherwise you run the risk of over-fitting your model — training the model with a limited dataset, to the point that it picks all the characteristics (both the signal and the noise) that are only true for that particular dataset. Note that a model that’s over fitted for a specific data set will perform miserably when you run it on other datasets. A test dataset ensures a valid way to accurately measure your model’s performance.
4. Building the Model
Building your own predictive analytics model requires expertise in data science. You will need the help of data scientists or someone with advanced analytics skills to build predictive models from scratch. Note that you also have the options of outsourcing this work to a consulting firm that provides analytics services or seeking connections with researchers at universities for their support.
However, if cost concerns prevent your small business from engaging experts, there are many software solutions available that come embedded with predictive modelling tools.
Though these tools may not offer the advanced knowledge that a skilled data scientist can bring in, they offer built-in predictive models, are easy-to-use, and come at a lower price point. Predictive analytics software can be a good starting point for small businesses trying their hand at forecasting.
5. Deploying the Model
Once you are done with building the profit margin model, you have to deploy it in order to reap its benefits. That process may require co-ordination with other departments. You have to aim at building a deployable model. Also be sure you know how to present your results to the business stakeholders in an understandable and convincing way so they adopt your model.
After the model is deployed, you’ll need to monitor its performance and continue improving it. Most models decay after a certain period of time. Keep your model up to date by refreshing it with newly available data still gotten through time series analysis.
Challenges of Using Time – Series Analysis in Preparing Predictive Analytics
Using time series analysis to implement predictive modelling tools is not without its hurdles. Here are some of the challenges that you might face on your journey.
a. Time Series Analysis and Predictive Analytics forecast probabilities —not certainty
No matter how much you would like data to help you make accurate predictions, what it actually provides is the likelihood of an event. Have it in mind that all predictions, including those done using the right data, leave some element of error or uncertainty.
Hence, the final call on any business decision should be based on a combination of elements—results data, your judgment, the value or impact of the decision, etc.—and should not be limited to one aspect alone.
b. Time series analysis take time
Time series analysis and predictive analytics cannot be implemented overnight. Acquiring data takes much time and implementing robust predictive models can take weeks, or maybe even months, depending on the level of expertise and knowledge you start with. However, be patient while you constantly test your models and learn the nuances of forecasting. Robust, reusable predictive models provide you revenue gains and cost savings for a long time.
c. Process Costs—tools, training, and testing systems
While costs of required software have gone down in the last few years, they are still costly. Note that you will also need to invest in training your employees on various time series and predictive analytics concepts. According to reports, some businesses may have to spend anywhere around $8,000 – $20,000 annually to implement predictive analytics, excluding training costs.
Businesses with 500 to 5,000 employees may have to invest up to $100,000 annually on predictive analytics, while for larger enterprises the investment needed could be $500,000 and upward.
Using time-series analysis to implement and forecast the profit margin of a business is popular in this age. However, consider experimenting with predictive analytics on a small scale and expand as you gain experience and see favourable results. You can start by identifying business cases where predictive analytics has already been successfully used (e.g., reducing customer churn or predicting customer demand) and adapt them to your business.