Do you want to forecast where your business will be in the next one or two years? Well there’s no better way of doing it than by using predictive analysis. Your organization can make use of different patterns contained within your data to provide immediate value in many different ways.

From predicting future performance, to quantifying and calculating risk, to even providing insight on the best course of action using the data’s trends. Here are 9 ways to forecast outcomes using predictive analysis:

1) Sales Performance Predictive Analysis

After hiring staff and growing business operations it’s time to make the actual sales. Predictive modeling uses techniques like statistical regression to look at your data and anticipate what you might expect to get.

Predictive analysis tools have the power to predict the potential sales you can make by reviewing past sales patterns made at different times throughout your sales history.

In the case where you have salespeople, you can also predict the individual sales performance each staff-member is likely to produce by looking at their prior sales histories. Targets that are likely to be hit can be easily boosted, while those likely to be missed can enable us to take swift action to avoid bigger losses.

Additionally, the right business strategy can be taken after predicting potential future sales accurately.

2) Ecommerce Activity & Web Analytics

Business is about better profits and increased returns. In today’s business environment where online marketing and ecommerce have become so common, your company or business unit may require more sophisticated tools and services to boost its market opportunities. These would fall under the analysis area of web analytics services.

Even web analytics audits can ensure that data is correctly collected, analyzed, and implemented to expand your business. This is all being done today with increasing frequency. Predictive analysis in this arena will undoubtedly help to boost traffic on your website by better understanding what leads to higher traffic.

Other online datasets and signals can also be instrumental in predicting the future of a business by following sentiment, ratings, reviews, and what customers are saying online. Something as simple as a decreasing Glassdoor ratings can be very powerful predictors of where a company is headed financially.

3) Lead Generation Predictive Analysis

Increasingly, companies are leveraging new algorithms to help themselves or their BI consultant find qualified leads based on that company’s potential customers’ ability to buy. This in turn enables these groups to make more qualified customers more interested in their product.

In many cases, identifying new and different places to engage with them online in new and meaningful ways. As long as you have the right audience, you can have the right leads. A poor lead generation program can cost a company a fortune in wasted time if they aren’t reaching the right people, and for what?

Today we have the power to use predictive analysis and business intelligence to enable and empower a well-versed business development or sales professionals to do the right kinds of focused marketing.

Furthermore, things like knowing client churn rate, or even what percentage of existing buyers might refer their friends or colleagues to purchase your product; allows you to more comfortably predict business pipelines.

Insights from predictive analyses will help you focus on what brings you more profit and improve on what brings you less. Lead generation expands on that by providing increasing opportunities to understand and better serve your loyal buyers while having the natural benefit of optimizing sales efforts.

4) Testing and Optimizing Marketing Campaigns

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Optimizing on your marketing campaigns may be a bit cumbersome at times, but the outcomes are almost always well worth the efforts with their quick, straight forward, and incremental results.

Often times, optimizing marketing campaigns requires the implementation of testing. Whether A/B testing or Multivariate, they always involve testing a hypothesis where you can get a positive outcome or a null hypothesis.

However, the beauty isn’t always in the test itself, but rather the creation of new and different data points with meaningful variances. These can become incredibly valuable events that can help better train your predictive models with incredible accuracy.

Testing is by far one of the most underserved areas of the analytics world today. It’s the definition of doing scientific research for meaningful qualitative and quantitative results that will improve performance.

Predictive analysis takes testing to new heights by essentially creating “virtual tests” when running historical data through the models. Many different inputs are ‘tested’ to understand their impacts on performance. This is fundamentally achieving the same goals, but usually with much higher accuracy.

Using predictive analysis to optimize marketing might be compared to driving a car by looking through the rear-view mirror… using the past to steer the future. But the alternative is akin to driving with a blindfold on. We’d be wise to take the rear-view mirror any day.

One more notable mention for optimizing marketing using predictive analysis is the real-time data we often have access to. The use of real-time data in forecasting future performance for the purposes of optimizing that performance comes in very handy for predictive modeling and can even be used to create machine learning algorithms due to the constant stream of incoming information to enhance and train these predictive models.

5) Managing Customer Relationships (CRM)

Without customers, a business fails. Today we have tools that assist in not only facilitating, but also measuring the effectiveness of every customer-related interaction. These tools are highly effective in their purpose to facilitate better business relationships.

CRM analytics (customer relationship management) helps you interact with your customers more effectively AND efficiently. Data collection and analysis become a lot easier using CRM analytics, and identifying business gaps becomes clearer.

The ability to measure the existing customer marketing practices helps predict the future and is an important predictive modeling strategy today. Many downstream results are directly impacted by upstream factors.

For example: the relationship between number of leads being entered into a CRM platform, and the total outstanding opportunity amount with a company’s existing book of business are in direct correlation -the question we can answer, is by much. Predictive analysis can take advantage of this data to show where the ship is headed.

6) Operational Efficiency & Process Mapping with Predictive Analysis

Being able to see what is happening in real-time increases the potential for optimizing the present to meet future goals. Using special tools for data mining and data aggregation, you can uncover some incredible insights that can directly impact business operations, namely operational efficiency.

Every organization is a combination of People, Process, and Technology. Often the processes are how people interact with the technology. Use of real-time data analysts and scientists is instrumental in finding efficiency gains in many areas.

Even more critical might be enacting models to predict the need for process improvements before waiting until existing processes have become unmanageable. Operational analytics aims at predicting future behaviors and analyzing as much data as possible to get insight.

Operational analytics can be applied in various work fields to predict the future. Business, scientific, information technologists, basically every field can apply these techniques to improve outcomes.

“Process mapping” is a new and emerging field of Operational Analytics that focuses specifically on modelling and optimizing processes and process changes for organizations, and measuring their potential impact.

Software platforms and tools like ARENA and Celonis are just two of many different technologies that can even layer on process improvement simulations.

7) Data Discovery

You already have your company’s historical data. Now you need to get a general overview of the data before focusing on details. You may not be a highly skilled data practitioner yourself, but finding trends with the right help is a high probability.

Conducting data research enables you to know your customers, what they like, and what they don’t like. It also helps you predict the seasonality of your business, both low and high.

To do data research, you don’t require machine learning and other forecast tools, but having them will drastically increase your likelihood of building a successful business. The right business decision can be made from well-analyzed data.

It gives an insight on what to improve and what to maintain.

8) Choosing The Right Modelling Techniques For Your Business

Before starting a predictive modeling process, identify the business goals. Look at the data sets available, the scope of the business, and the results expected.

Be sure to get the best model made for your business since you will deploy it for daily use.
Some predictive techniques can include things like decision trees, while others are only designed to analyze numerical data like linear regression. Therefore choosing the right technique for the goal is critical.

9) Human Resource Analysis (satisfaction and turnover)

One of the most interesting new areas of predictive analysis is within the function of Human Resources. Not only is HR rife with valuable employee data, but that data can be analyzed to understand everything from worker satisfaction to turnover, and even predicting employee disengagement and departure.

This is one area of analysis where practitioners are wise to heed ethical analytics practices and uphold important standards of individual data privacy.

Conclusion

Making the right predictions by investing in a predictive analysis program will save your company incredible amounts of time and money. Statistical algorithms and artificial intelligence techniques allow analytics companies to offer these services to make predictive modeling easy and painless.

It’s okay to not understand how to analyze data yourself, but it’s important to replace that gap with experienced practitioners to do the analysis for you. This way you can focus on business strategy and let the practitioners collect, analyze, and present data in the simplest way for you to understand to make that data actionable.

Choose the right company to do your predictive modeling and forecasting and retain  expert help to predict future outcomes for your organization by getting the best insights for the best decisions.

Proper advice on what will work for your venture and what will fail is highly essential for small and big business alike. Creating a logical layer between the data and getting a solution for the same is a recommended way to forecast business outcomes using predictive modeling. Consider Insights Analytics.