Startups use data analytics to streamline their processes, target specific audiences, identify opportunities and achieve quick growth. Here are five ways predictive analytics can help startups and SMBs.
It’s a competitive world out there, even more so for budding businesses. Among the sea of already established organizations and brands, there are just as many — if not more — new operations springing up each day. It means that any startup, big or small, absolutely must gain a leg up if it has any hope to succeed.
It ties directly into one of the most important aspects of any successful business, which is creating a sustained or perpetual system of growth. It’s especially crucial for a new company not just to see a few wins here or there, but continual support from a growing customer base. Without that, there’s no way a startup can spread its wings and fly.
Unfortunately, it’s also an incredible challenge to build a support system for a new business, earning continual revenue. To make matters worse, in today’s landscape, customers want instant gratification. Brands must adapt to deliver when, where and how their audience wants service. Many people are quick to try a new company, especially those that offer additional benefits at a lower price, yet just as ready to discard them after a poor or mediocre experience.
What Is 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, analyzed and extracted. The primary goal of the entire platform 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 Predictive Analytics Help Startups?
Imagine understanding precisely what customers want, or how they’re going to react to a particular decision or product launch. Consider a solution that can tell in incredible detail how to target or engage with a subset of an audience.
That’s what predictive analytics can do. A proper system will utilize machine learning to understand data it’s ingesting. It generally contains historical information, mainly performance-based, which helps make sense of present data and informs future predictions.
Essentially, data is a currency because, without it, the predictive models cannot exist. The trick is collecting enough information to put together solid models, especially for new startups with small customer bases.
The solution is still a predictive analytics program, just one that’s service-based and offered by a major provider such as IBM or Amazon. Analytics providers have well-tested platforms in place that have been optimized with the help of larger existing businesses or partners. There are several analytics tools that can be used by small businesses.
The main selling point of predictive analytics is that it helps businesses, big and small, achieve continual growth. Since growing a company is no small feat, any operation that hopes to build a future-proof system must focus on it. Perpetual expansion helps develop the necessary foundations, which ultimately lead to future success.
Predictive Analytics in Action
One question remains: 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.
Combine With A/B Testing for Maximum Success
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.
Kolabtree’s global pool of freelance data analysts and machine learning consultants have helped several startups and small businesses use predictive analytics and forecasting to improve business. It’s free to post your project and get started.