Predictive Analytics

Analytics is a rather broad term that refers to a range of statistical information that can be applied in various ways. Data analytics deals with digital content collected over time via metrics tracking solutions — that is mainly amassed, analysed and extracted. The primary goal of the entire process is to come up with actionable Intel that can inform existing or future operations.

To be more specific, predictive analytics is a similar technique that involves using collected data to build accurate models of future events. One might do things like:

• Forecast stock or investment prices
• Estimate demand changes for products and goods
• Identify the source of major losses such as theft or fraud touchpoints
• Predict new customer opportunities, including new demographics for marketing campaigns
• A critical component of predictive analytics is that it’s not a faith-based system. Instead, predictive models are wholly accurate, because they’re built from a substantial database of current, valid information. In other words, while there are no guarantees, it’s about as close as one can get.

How can a small business utilize predictive analytics and machine learning? 

1. Customer Service Improvements

Even the most successful companies still have a lot to learn from their customers, particularly in what they want in terms of support. Do they want same-day or fast delivery options, for example? Is it necessary to launch a live, always-on communication channel? Are the company’s products and services meeting customer needs, and if not, what has to change to make it so?

By ingesting and extracting insights from customer performance data, businesses can really dig into the needs of the average consumer.

2. Better Demand Preparedness

Most companies experience a lull in demand offset by significant increases throughout the year, primarily because of the current season. Other factors play a role as well, including prices, current events, new product launches and more.

Predictive analytics can help plan for demand trends, allowing a business to better prepare for the shifting tides. When demand drops, inventory replenishment processes will slow to reduce waste and lower costs. Adversely, when it skyrockets, then everything can be scaled up to address the change. The best part is that machine learning solutions can help automate a lot of the operations.

3. Optimized Product Management

While startups may generally launch with just one or two products, over time, it makes sense that inventory would expand. The problem with product launches is that there are never any guarantees. 

However, predictive analytics can help discern whether or not planned launches are going to sell, and whether or not customers will be receptive to new ideas. That’s important, especially for startups with limited capital, because it’s necessary to reduce the risk of failure and losses. One boggled launch often means the difference between a stable or failing business.

4. Targeted Marketing 

Usually, a startup focuses on a niche or smaller audience segment and then eventually branches out after meeting with success. It limits risk, but it also provides a much safer route of growth.

With a predictive analytics system, however, businesses can understand potential audiences in greater detail. Not only does this mean fine-tuning experiences and marketing to a specific group, but also branching out to new demographics. The analytics solution can dig in and find new customers that might be interested in a product, and may even have some suggestions on how to engage or target them.

5. Product Quality Enhancements

Sometimes, when it comes to developing a product or choosing suppliers, the quality of applied materials makes all the difference. Swapping from one supplier to another, for example, might result in a quality drop for produced goods. 

The changes in quality might not always be apparent, at least not without customer feedback. That’s where predictive analytics can help. Data tools can discern whether or not specific changes will be good or bad, how customers might react and more. It can also be used to scoop up and summarize customer feedback faster when there is a major change. The result is a more reactive business in terms of generating customer satisfaction.

While predictive analytics might help businesses gain a better understanding of what customers are doing and how, the why still tends to remain a mystery. That’s where causal inference or A/B testing comes into play. By combining the two practices — predictive analytics and A/B testing — a business can become a tour de force.

It’s all about anticipating the needs of existing and potential customers to build positive growth. In the end, a sustained channel of support is what helps any business stay afloat. Predictive analytics is a necessary foothold for achieving such a thing.