Part of the result of sophisticated algorithms, machine learning and artificial intelligence is the evolution of predictive analytics. The ability to anticipate outcomes, improve operations or foresee potential customer activity is of great value to businesses across multiple sectors. However, once you receive the information, are you using the output to its highest benefit?
To help determine whether your company is using predictive analytics effectively, here are some best practices to guide your process.
Understand the Goal
Predictive analytics are best used when the goal is well defined. You need to focus on a specific problem and determine what information guides you to a solution. For example, customer sales data can determine the direction of marketing campaigns or refine cross-selling efforts to be more effective. By focusing on an area, you can analyze the data to meet current needs. Otherwise, sales data analysis may note patterns that aren’t relevant to your current needs.
Get to Know Your Data
Part of the approach needs to be based on the amount of data involved. Large amounts of data collected over years, or even decades, must be refined to provide the most use. However, businesses with limited amounts of data need to consider what is available for analysis before defining a goal.
If the hope is to improve cross-selling opportunities, this will be more challenging for businesses with only a few months of sales on record. However, a business with multiple years of sales data can likely find useful patterns to guide future efforts.
The size of your business may dictate the size of your target. For example, a small retail business focused on a particular product niche may choose to analyze all customer data when determining new sales initiatives. However, a large corporation with multiple product lines and a diverse customer base might find better results by targeting specific demographics within its predictive analytics model.
By approaching a smaller target, you can make large stores of data more manageable throughout the process. And the results you receive are better equipped to lead to meaningful change for optimal outcomes.
Each initiative needs to progress based on the defined goals. Just as IT projects can become victims of scope creep, predictive analytics are also vulnerable to add-ons. If you overextend, the quality of the analytics lose accuracy which impacts their overall value.
However, if an opportunity for refinement presents itself, changes of that nature may be appropriate. This is especially true if the results apply to a broad category or fail to target the initial goal with enough specificity.
Test, Refine, Repeat
Your first attempt at reaching a goal through predictive analytics may not go as planned. As with many IT implementations, it is important to monitor milestones and refine processes accordingly. It’s also important to recognize when an algorithm is failing to produce results, and determine whether starting from scratch is a better approach than tweaking a flawed approach.
Work With Experts
Predictive modeling is a complex specialty, so a person with only a fundamental understanding of statistics or descriptive analytics might not have the knowledge to create the models you require. Often, it is better to enlist the help of an analytics expert early in the process than to waste time trying to push someone to produce results beyond their current capabilities.
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