Predictive Analysis – Marketing 2.0 for Smart Marketers

Predictive Analysis - Marketing 2.0 for Smart Marketers

Marketing, when powered by insights into customers and their buying pattern, places the business in the center of users’ life. Using predictive analysis, companies are doing just that – becoming an irreplaceable part.

The aim of all marketing efforts across a range of industries has now shifted. What was once concentrated on getting a message to people who would act on it in company’s benefit has today become knowing what the customers need today and what would they need tomorrow.

The thought process of today’s marketers has now shifted to knowing what the users would like to buy next and then seeing to it that your business is prepared for it. Today, only those business can expect to survive who know how their customers would behave even before they actually do.

The technology revolution that has been favoring this switch is Predictive Analysis. And this article is focused entirely on it.

In this article we will look into what predictive analysis is, what is the one component that fuels it, the benefits that it offers to the customers, and lastly the strategy that you should employ for marketing predictive analysis.

What is Predictive Analysis

Predictive Analysis is a subset of Artificial Intelligence that deals in analyzing old data with the help of machine learning and statistics to predict what steps customers would take next.

As humans, we are an extremely predictable set of people. Every step that we take from the moment we wake up to the time when we sleep, is a reflection of some routine. This predictability of ours is a goldmine for the marketers all around the world.

Suppose you are a B2C Marketer in the US, now you know that the period between November till January end would be extremely busy for you since it is the holiday period. And as a B2B Marketer, you would know that the sales would be low as everyone will be occupied with festivities. This analysis that you do as a marketer is predictive analysis.

Taking this to the next level, knowing which product will sell most and which you would run out of first, is the next level of marketing predictive analysis which is done with the help of algorithms and it is exactly we are going to discuss in this article, today.

Next to knowing what Predictive Analysis means for, is knowing the essence it is based on – Data.

Place of Data in Predictive Analysis

The moment we switch on our internet to perform even the simplest of activity, even when we are only online to check our social media platforms, we give out a trail of data that is a direct insight into who we are.

This data that we leave out can be anywhere – our social media, the review page of abc product, the blog we wrote on WordPress. Gathering all these data that we leave behind and finding relational links between them is what all the latest disruptive technologies including Artificial Intelligence and Predictive Analysis is based on.

Now, when we talk about data in context of Predictive Analysis, you can only make the most of it – whether you are looking for an efficient social media marketing program or seeking statistics based guidance for developing a product – when you fulfill two very important points:

Data that is very comprehensive

In order to develop a successful predictive analysis strategy in your marketing process, it is imperative that you have a varied amount of data about your target audience. The aim should be to collect data as a B2C marketer if you are a B2B marketer and vice versa.

The point I am trying to make here is that as marketers you should have as much data as you can find about your customer group.

Presence of an Algorithm to Help Find Relation between Data

When you have a set of comprehensive data, no matter how unrelated, the next step is to have a machine learning algorithm that would analyse all the data and find relations between them.

Until you find out relation in the data to fit in your product or service, your data would remain incomplete in the marketing sense.

Now that we have looked into the concept of Predictive Analysis and the necessities of the data that the whole analysis would be based on, we will now move on to its impact on the present day’s and future business marketing goal.

Benefits of Predictive Analysis in Modern Day Marketing

1) Better Insights

Predictive Analysis takes us further from knowing where the prospects into a zone where we know what they are doing there, what posts they are reacting on social media, what marketing efforts they are dissing. With such detailed information into our customer’s lives coming into reach, we marketers now know prospects as clearly as we know a close friend.

2) Better Promotional Effectiveness

Through the technology, businesses now know which of their promotional efforts are actually working and with whom. Till a few months or years back the profitability of the promotional effectiveness could only be measured by seeing the traffic coming on website using the social media statistics or a company’s google analytics, which was undoubtedly a good enough thing, but the situation has again changed today.

Today, businesses now know which promotional medium is working in their favor, which age group or demographic is answering to that, at what time, what is the cost that is going behind the specific high returns advertisement medium etc.

3) Better Relationship Management

By knowing exactly how customers are reacting to your product on social media or by studying their purchase history to gauge if they are happy with your service or not, you can develop strategies around keeping your loyal customers engaged and your once loyal customers reengaged.

When it comes to customer relationship management, predictive analysis don’t just help you with identifying who they are but also with devising strategies that would actually give a positive actionable response to.

Let’s sum up the article with a predictive analysis strategy that marketers follow across both – their direct or multi channel social media marketing efforts.

Analyze and estimate customer behavior: Make an in-depth study of their past buying habits and the seasonality/trend of the time and show them products accordingly on your e-commerce site. Also, you can take this to the next level by showing them the ads of the specific product on social media.

Conduct the “what next” scenarios: Use the power of predictive analysis to answer the many probabilities arising from the what next scenario. When done right, it will give you an exact estimate of which product will run out of stock first and what alternative would people would look for. This analysis can help you in both planning your inventory and setting a price range to your alternate product.

Develop effective advertising and marketing strategy: Predictive Analysis helps you develop an effective marketing and promotion strategy. By giving you insights into where your prospective buyer is and what advertising medium would he be most inclined to base his purchase on, the technology prepares you to become a marketing ninja, as long as you are prepared for it.

Employ the strategies for getting repeat business: Predictive Analysis, when employed right, tells you exactly which users would buy your product again or which of them would re-subscribe your service. In the time when marketing budget is becoming one of the most thought over and carefully planned part of a business plan, an information pertaining to which customer or customer group would repurchase can help businesses create more concentrated strategies to keep the returning custom segment engaged.

Classify customers according to your business goal: Using predictive analysis, businesses can classify their customers according to not just the ones who would repurchase a product but also in ones who has the maximum probability of answering to advertisement or spend x amount in buying a product.

Using this information, businesses can classify their target audience according to the business goal and take their targeting efforts to an altogether different level.

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