Traditional analytics are antiquated, time-consuming, and ineffective — and, if you’re still using them, it’s time to stop! Even the simplest tools of predictive analytics can be used to win in the world of optimization. User-friendly for even low-tech marketers, there’s no need to dive into the ocean of predictive analytics without a lifejacket. Grab the low-hanging fruit first. Here’s how.

Locating the Right Person
It’s all about finding the right person, or audience, and creating a uniquely personalized experience. Your organization can use predictive analytics to gauge the actions a customer is likely to take — whether a long-time customer, a potential customer, or a customer who is withdrawing from your brand (churn). These customers, with all of their various qualities and desires, comprise your audience base — and, all of them are valuable to your organization. The incredible thing about predictive analytics is that it can be used to guide your target audience down the path of least resistance. Meaning, you can create optimized experiences for loyal, prospective, and withdrawing customers — singlehandedly increasing the likelihood that your entire audience base remains engaged with your brand. In all reality, old-fashioned marketing simply cannot hold a candle to the new age of predictive analytics.

Identifying the Right Content
Next, it’s time to cater to your customers’ needs. It’s time to figure out what your customers care about, or which content appeals to them. Let’s say, you’ve geared up the top 1,000 people who are most likely to purchase a certain set of products. You can use predictive analytics to effectively match those customers with the most appropriate product offers or services. In this case, if 1,000 customers purchased bicycles, you wouldn’t send them product offers for pogo sticks — that simply wouldn’t resonate with their buying habits. Instead, offering them deals on additional bicycle products would be a better idea.

Determining the Right Channel
Choosing the right channel of communication is important when reaching out to particular sets of customers. Let’s use financial services as an example. Let’s say, you’ve effectively predicted which customers are likely to churn, and you’ve cross-tabulated that data with their lifetime values. Those customers and the ways you reach out to them are going to be different from the ways you reach out to your other audiences. For those customers who are highly likely to churn and have high lifetime values, it may be best to choose a more personal approach such as calling them directly versus shooting them emails. A personal phone call shows appreciation of lifetime patronage. On the other hand, customers who have low lifetime values and high likelihoods of churn should be allowed to withdraw, as they’re costing you your business. Alternatively, customers who don’t have high likelihoods of churn but lower lifetime values could be upsold new products or services through low-cost methods — website or email, for instance. It’s all about establishing the appropriate channel of communication based on the values you attribute to various customers. That’s the beauty of predictive analytics.

Finding the Right Time
Time: it’s so important, but there never seems to be enough of it. This is an aspect of marketing that seems to fall through the cracks repeatedly, and yet — timing is everything. Every organization must go through the motions — sales and research, product consideration, purchasing, and retention and loyalty. The key is to understand timing and move your customers through your intended marketing cycles at the right time, ensuring that you don’t miss a window of opportunity. This means you evade the possibility of your competitor sweeping in and nabbing your customers out from under you.

Driving Your Business Goals
The prerogative of your organization is to drive business goals by predicting customer futures — more specifically, their futures with your business. When you know what is most likely to occur, you’re able to anticipate how your company will perform next week, next month, or next year. More importantly, predictive analytics enables you to understand your performance at an aggregate level as well as what drives business goals. This means knowing what aspects of your business are most controllable, what actionable leverage you actually possess, and how you can effectively influence your key performance indicators. As the customer journey becomes more complicated, powerful marketing tools and techniques are necessary for tracking your business goals. Predictive analytics helps you take the gut feeling out of marketing, so you can move toward a more powerful understanding of your revenue flow and then proactively create an even brighter future.

Using the Simplest Tools
Ultimately, the goal of predictive analytics is to engage your audience with optimized experiences. Get to know your customer. Engage with them through these optimized experiences using even the simplest tools of predictive analytics.

To illustrate the value of employing even the simplest of predictive analytics tools, let’s take a look at contribution analysis. It intelligently identifies hidden patterns or contributing factors for statistical anomalies in your data. Contribution analysis performs tens of millions of queries against your datasets. From there, machine learning can be used to provide you with a visual narrative that illustrates why your data does what it does. In the past, it would have taken weeks to analyze datasets of this enormity — but, within a matter of seconds, contribution analysis accounts for every sudden spike, deviation, or abnormality in your data. Even the simplest predictive analytics tools are cutting edge compared with traditional analytics.

If you’re still living in the world of traditional analytics, it’s time to step up your game and delve into higher levels of sophistication. Gut-based decision making is a thing of the past. The good news? You don’t have to consume the entire ocean of sophisticated approaches — as daunting as that seems. Even the simplest tools of predictive analytics will outperform the traditional approach.

Source: Adobe Blogs

Kalyan Banga

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