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Guidance and results for the data-rich, yet information-poor.

Category: Articles

Strategic Issues That Should Be Addressed When Considering Predictive Analytics

One of the common roadblocks to successful model development or process implementation is how most leadership insist on immediate results and return on their investment in analytics. Instead of taking time to design and develop each analytic model, it is an all too familiar scene when management demands implementation in impractical time frames.

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Successful Predictive Analytics Begins With Training

predictive analytics

Ideal results in Predictive Analytics projects are produced when an organization has members that are experts in either analytics or in their own domain. While it’s not necessary—or realistic—to have someone who is fully proficient in both, it can be a definite boon to have basic knowledge across general areas.

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Which Business Problems Can Predictive Analytics Solve Best?

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One of the most common questions that get asked about predictive analytics is, “Where do we begin?”. Before anything else – software, techniques, and even training – your business needs to decide on what kind of problem to solve first. What problem will provide the best ROI when predictive analytics is applied to it?

A solid area to focus on is in your organization’s daily operations. This kind of single transaction/interaction with a customer is also called an ‘operational decision’. Wondering what the best offer you could make to a customer in order to keep them or judging whether a claim is fraudulent falls under this type of decision. This is a practical area to begin with because single transactions make it easier to create effective predictive analytic models.

Ever single order, application or claim requires that a decision be made. Because of their simple and straightforward nature, there are fewer challenges to building predictive models based on these transactions. The high volume of transactions means there is a wealth of data available for analysts to work with. More often than not, there will be a significant amount of data for each customer, with every additional transaction contributing more data. Businesses that hope to gain insights on customer behavior over time will then greatly benefit from relying on predictive analytics.

With these transactions, only a set number of actions can be observed, making it easier to link outcomes or results to specific choices. This will then build a defined feedback loop that helps your business improve predictions as time passes.

The sheer volume of orders or transactions finally allows a lot of room for testing and experimentation. For example, it makes it easier to try variations of approaches to cross-selling with different orders to see which approaches are most effective. The resulting data collected also greatly improves the quality of succeeding predictions. Begin your journey to predictive analytics success by working on improving your day-to-day transactions!

3 Mistakes Leadership and Practitioners Make In Predictive Analytics

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The buzz about Predictive Analytics has not died down, and the organizations who have experienced its potential all agree: the discipline is here to stay. Its mainstream adoption is due to the success experienced by organizations that have properly implemented projects, but many more have held back due to a preconceived complexity. Both leadership and practitioners alike have been guilty of making crucial mistakes, preventing their businesses from reaping the rewards from predictive analytics. Here are a few of them:

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Own The Holidays: Prepare Your Retail Business With Predictive Analytics

Own The Holidays: Prepare Your Retail Business with Predictive Analytics

Last holiday season, a mass of retailers collected a large volume of data from their customers. It is fairly typical to delay using this data during the holidays itself, but once the hectic days pass, preparations for the next year need to begin. You can gain a tremendous advantage over your competition by taking the lessons observed from analyzing past data collected.

When you combine and analyze different types of data, you can then realize actionable insights on how your business can provide an outstanding shopping experience that will delight existing customers and gain new loyal ones.

Take a look at some ways retail companies can make use of past holiday data to prepare for the upcoming season:

  1. Understand Past Trends And Adjust Operations Accordingly

Data collected from previous holiday seasons can significantly help retailers understand shopping trends and make the best adjustments for the coming season. One example is to analyze the flow of customers and how they move about the shop. With this, retailers are able to adjust displays and endcaps to boost sales. With the additional help of video and point-of-sale analytics, retailers are then able to understand which products sold best, which ones didn’t, and most importantly, how customers reacted to various sale offers.

  1. Optimize The Shopping Experience

With analytics, retailers aare able to compare the size of groups of people moving through a shop  to the amount of products sold. Using the insight gained from this analysis, companies can strategize on how to improve the destination experience for customers, leading to longer times spent inside the store and interacting with the items, and finally, boosting sales.

  1. Improve Employee Training Programs

The use of video analytics provides great insights into the interactions between customers and store employees. Human behavioral models work hand in hand with analytics in measuring satisfaction, motivation and sentiment for both associates and customers – all done in real-time. Aside from assisting management to helping in improving sales, the results of analysis can be used to improve training programs all around.

With the data collected during the holiday seasons, businesses can significantly boost sales and operations in leaps and bounds, greatly improving the retail shopping experience every day of the year.