What is Predictive Analytics and How Can You Apply It for Lead Generation?
No texts of grandeur are required to articulate the influence analytics has in marketing. Not Google Analytics, but those wretched math laden analytics that many do not dare approach even with a ten-meter pole. However, achieving significant growth, efficiency and effectiveness can be attributed precisely to the dreaded graphs and calculus.
To grasp the potential of predictive analytics for your business’s lead generation, it is important to ask what, why, and how.
What is predictive analytics in marketing?
Predictive analytics is an umbrella term that combines various statistical analysis techniques and machine learning algorithms for the sole task of identifying patterns. The process is based on feeding current and past data in order to extract probable future behaviors. Consequently, under eyes of scrutiny, these future trends are displayed neatly to respective management without the overbearing strain of big numbers.
Analytical modelling can be further split into: prescriptive and descriptive. Prescriptive models serve to predict predefined target variables for each object. A common use case of prescriptive models are campaigns aimed at getting the customers to renew their order. In contrast, description models are not limited by a predefined outcome and serve more strategic purposes. One such general use is assistance in segmenting various types of customers.
In addition, different machine learning algorithms can be put to the task so you can’t feel constrained by limited choice. Answering the question of which algorithm to select is based on the desired output from the model (prescriptive or descriptive). The three categories that embody the available selection of algorithms: supervised machine learning, unsupervised machine learning, reinforcement learning, yet I will not dive into each in this article.
Why use predictive analytics in marketing?
Many marketing questions are left for your team to interpret based on their knowledge. However, enhancing their decision making with AI power can facilitate more accurate results and achieve a positive feedback loop. As the algorithms uncover more possibilities from the existing data you might be fascinated by the potential that was always there but unable to be harvested. Besides, it is also wise to entertain the thought of an expert leaving your company. His/her expertise will no longer be available to you, hence you lose the full understanding of your audience — one that may be crucial for shaping some of your future actions. However, when using predictive analytics, you can retain your marketing efforts since the model has no legs to run away.
Here is a list of applications for predictive and prescriptive as well as the questions they solve:
This is just a small template sample of what analytics can do. With just a few modifications, it can be made to suit custom purposes of different scopes. But you need the full package of people and data to implement it. If you miss at least one part the result is going to be lacking.
How to use predictive analytics in marketing?
Predictive analytics is a good choice when working on pre-sale issues as it can improve and enhance the accuracy of lead generation and scoring processes. You add one more layer, since most companies are already using big data around their fingers to point at the “right” customer. But the lack of automation means the cycle can be quite tedious and bottlenecked by the availability of specialists.
At the moment few companies delve into the depths of AI and keep on basing their marketing decisions on intuition and professional experience. It is mostly art hidden behind scraps of science scarcely used for argument’s sake so it doesn’t seem like a madman’s decision. Granular data sets are threaded to employees desperately trying to make something off them. The main idea of combining customers’ previous history and external data input to generate a wider field of view on the prospect stays the same. It’s the workforce being shifted to meritable algorithms that bring to light what factors truly compel leads to convert. Therefore, the output serves as a pillar for science-based strategies.
Sundry companies had to turn their plans 180 degrees as big-data analytics highlighted that the focus should have been on a different persona. The more quantity and quality data you feed the greater insights you can expect. It is exactly here where businesses stumble across what seems a minor flaw, but is what setbacks many from implementing such measures.
Your predictive model is as good as the data you provide
Predictive lead generation is the strategic effort, based on predictive analytics, that focuses on the identification of ideal customer profiles discovering B2B prospects that fit that profile. However, for an accurate model, you need to base it on a reliable data set depicting your clients’ behaviour in the past. Similarly to the situation when you’re not so sure about the accuracy of the information you hold, is the one when you have no previous records at all, especially when speaking about a new product or if yours is a new company. Wasting money for optimizing the algorithm will not net you any benefit unless you fix the underlying problem that we all have at one point in time pay attention to. Empty fields, obsolete details, incomplete CRM variables are bent to stop you in your track.
Prominent B2B databases, have streams of data at the ready to funnel into your machines and enhance siphoning processes. If you choose a high-quality provider, there will be no more obsolete data poisoning your results since reliable databases are updated regularly. Prospects that were not identified due to lack of data for a suitable model will be provided in lists, businesses that were unusable due to empty fields are no longer a threat thanks to data enrichment. The sheer amount of extra information starting with company profile and ending in exhaustive financial reports, coupled with technologies implemented enables you to reach a desired 360-degree view of the customer. All that is left is to identify the factors important to your business and base your model on them.