Predictions have always piqued our interest. Maybe that’s because predictions are so hard to make and even harder to get right.
But, making the right market predictions will save your business tons of money and will deliver superior results.
But how do you make predictions such as these and identify which are the accurate—more realistic—predictions?
The answer is simple: Data.
With the right data, you can predict future business outcomes with greater accuracy.
But, successfully implementing predictive analytics is a big challenge, especially for small businesses with limited data management resources. Collecting, organizing, and storing the right data are prerequisites to adopting predictive analytics.
You must follow this disciplined data approach to be able to make more accurate forecasts; poor data collection and preparation results in watered-down prediction models, which will lead to bad business decisions.
This article will discuss how to use predictive analytics in different business scenarios along with real-world examples. We’ll also walk you through the steps to successfully embed predictive analytics into your business process.
What is predictive analytics?
Gartner defines predictive analytics as a form of advanced analytics that examines data or content to answer questions such as, “What is likely to happen?”
Predictive analytics uses historical data, artificial intelligence, and machine learning to predict future outcomes.
Predictive analytics solutions use statistical tools such as regression analysis, data modeling, forecasting, and multivariate statistics to answer questions around what will probably happen in the future.
How to use predictive analytics in different business scenarios: 3 examples
Predictive analytics enables you to find solutions to different business challenges as well as helps you achieve your business goals. Here are some scenarios where predictive analytics boosts business outcomes:
1. Predictive analytics helps reduce customer churn through personalized offerings
Acquiring a new customer is seven times more costly than retaining one. Being able to minimize customer churn helps business reduce costs as well as build a larger loyal customer base.
Spotting dissatisfied customers and personalizing offerings for them is one of the most common situations in which predictive analytics is applied.
To calculate the “likelihood to churn” score, you’ll need 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 regression to identify customers that are likely to churn.
Based on the churn score results, you can prepare personalized promotion offers such as discounts, exclusive memberships, and other special concessions to woo these customers back into your fold.
Coyote optimizes its loyalty program with predictive analytics.
Business goal: Coyote, a real-time road information services provider, wanted to strengthen its customer base using an effective loyalty program. The firm wanted to segment its customers, qualify incoming data, and quantify device usage.
Action taken: Coyote used Dataiku Data Science Studio software to implement a predictive behavioral analysis tool to segment customers. The application automatically compiled heterogeneous data, such as real-time device data, contractual data, and customer details. The software then cleaned the data and used machine learning to model user behavior. The results were then used to optimize marketing campaigns.
Result: Coyote was able to segment its customer base with high accuracy, increase performance of outbound call campaigns by 11 percent, and reduce customer churn with personalized marketing campaigns.
Dataiku DSS’s customer churn prediction tool (Source)
2. Predictive analytics supports superior sales forecasting
Many business decisions, including hiring new people, setting up new offices, and stocking inventory, are made based on sales revenue forecasts.
But how often does your business get those sales predictions right, even within the permissible margin of error?
Traditional sales forecasts still rely heavily on gut feelings and limited data
Predictive analytics models that use internal and external data sources such as marketing automation data, historical sales data, prospect details, individual sales person’s win rates, etc. can forecast deals accurately around 82 percent of the time.
Data from multiple sources improves accuracy of sales revenue predictions.
Business challenge: A public service agency in the U.S. was facing continuous budget overruns due to inaccurate sales forecasting. It consistently overestimated demand, resulting in unnecessary costs in hiring and material resource purchases.
Action taken: Instead of solely relying on historical operations data, the agency collected more data through customer demand studies, market research, Google search trends, and historical sales information. They input this new and wider set of data into a forecasting model that they developed with the help of an external consultant.
Note: If using the services of a consultant is out of your budget, you can opt to use predictive analytics software applications that offer custom calculations and built-in models and support data from multiple sources.
Result: The new sales forecast model was 13 times more accurate and helped the agency anticipate consumer demand and adapt to market changes more quickly.
3. Predictive analytics tools enable you to set optimal prices for products
Setting the right price for your 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.
Predictive analytics can help you arrive at the best price at which to sell your goods and services.
Predicting consumer demand for your products, as well as understanding customer behavior, buying patterns, and market trends will help you optimize prices and improve revenue and inventory management.
Rue La La increases revenue by 10 percent through price optimization.
Business challenge: Rue La La, a boutique retail firm, often has to predict sales and set prices for products that are being sold in its online store for the first time, and therefore have no historical sales data. They often found such products either getting sold off within the first few hours after launch or not selling well at all, resulting in lost revenue.
Action taken: Rue La La developed a set of quantitative attributes for its products and used historical sales data to predict future demand. They built a demand prediction and price optimization model using statistical and computing technology, such as regression analysis and machine learning. Their automated pricing decision support tool was developed in collaboration with MIT (Massachusetts Institute of Technology).
Result: Implementing the optimized prices as suggested by the pricing tool helped to increase revenue by 10 to 13 percent across different departments.
Other areas (in multiple industry segments) where predictive analytics is useful include predicting:
- Employee churn
- Loan payment defaults
- Medical patients who are likely to return
- Machines that will need maintenance or replacement within an year
These are just a few of the many exciting possibilities that predictive analytics offers.
But before you jump in into the fascinating world of predictive analytics, there are few things you need to put in place.
Predictive analytics requires a data-driven culture: 5 steps to start
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.
5 steps to guide you as you prepare your business to adopt predictive analytics
1. Define the business result you want to achieve
Predictive analytics allows you to visualize future outcomes. Clearly defined objectives help to tailor predictive analytics solutions to give the best results.
Some examples of business questions to which predictive analytics 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 either have to work toward collecting relevant data over a period of time or modify your questions to tackle the same challenge from a different angle.
2. Collect relevant data from all available sources
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. Improve the quality of data using data cleaning techniques
“Garbage in, garbage out” is a computing term referring to the fact that low quality input generates poor output values.
Your predictions will be grossly inaccurate if your input data is poor. You must ensure that sales people, marketers, and other employees enter the right data values in the prescribed format. This helps to reduce the time spent cleaning and formatting the data.
You’ll also need to prevent and fix duplicate records as well as normalize data to ensure consistency in records. Most business intelligence software solutions offer data cleaning features such as data elimination, data standardization, data harmonization, and data profiling.
4. Choose predictive analytics solutions or build your own models to test the data
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.
You 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.
But, if cost concerns prevent your small business from engaging experts, there are many software solutions available that come embedded with predictive modeling 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.
Look for these key features when choosing predictive analytics software
|Data mining||Extracts data from multiple sources to provide relational information and identify patterns.|
|Data preparation||Analyzes and cleans data by using techniques such as de-duplication, data normalization, data wrangling, etc.|
|Predictive modeling||Uses statistical tools such as regression, correlation analysis, clustering, etc. to create complex equations that predict behaviors and outcomes.|
|Predictive insights||Uses machine learning and AI to process large values of data in real time and generate updated predictive scores whenever new events happen.|
|Data visualization||Provides charts, graphs, and maps to represent the predictive insights, making it easily readable.|
|Integration with R, Python||Integrates with popular programming tools for analytics such as R and Python to enable users to create their own models, tweak existing models, add larger data sets, etc.|
Check out our predictive analytics software directory to get a complete list of software solutions offering predictive capabilities. Compare these software options based on their features and pricing and read reviews written by real users before you make your purchase decision.
5. Evaluate and validate the predictive model to ensure robustness
Evaluating and validating your predictive model with alternate data sets allows you to identify weaknesses in the model, as well as helping ensure that the model works well under different scenarios.
Don’t worry: Even if you’re unfamiliar with these techniques, nowadays, most predictive analytics tools provide model validation capabilities within the software. You can use these automated features to check the robustness of your model.
Finally, embed the predictive models into your business processes and use the results to make better business decisions.
Be aware of the challenges of implementing predictive analytics solutions
Implementing predictive modeling tools is not without its hurdles. Here are some of the challenges that you might face on your predictive analytics journey.
Predictive analytics forecasts likelihood of an event—not certainty
However much you would like data to help you make certain and completely accurate predictions, what it actually provides is the likelihood of an event. All predictions, including those done using the right data, leave some element of error or uncertainty.
So, 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.
Building predictive analytics into your system can take a long time
Predictive analytics cannot be implemented overnight. Building and implementing robust predictive models can take weeks, or may be even months, depending on the level of expertise and knowledge you start with.
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.
There are costs involved in adopting predictive analytics—tools, training, and testing systems
While costs of predictive analytics software have gone down in the last few years, they are still costly. You’ll also need to invest in training your employees on various predictive analytics concepts.
Small businesses may have to spend anywhere between $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.
Your next steps for adopting predictive analytics
Start experimenting with predictive analytics on a small scale and expand as you gain experience and see favorable 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.
3 tips to help you kick-start your predictive analytics journey
Check out GetApp’s directory of predictive analytics software for a complete lists of software solutions that offers predicting capabilities.
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Note: The information contained in this article has been obtained from sources believed to be reliable.The applications selected are examples to show a feature in context, and are not intended as endorsements or recommendations.