Why does an internet marketer need predictive analytics?

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Content
Applications of Predictive Analytics
1. Attract more clients
2. Improve customer experience
Case from Lanit Omni: from pilot to productive operation
3. Predict advertising effectiveness
Predictive Analytics Tools in Marketing
Where to start?
You collect data, run analytics, and make decisions based on it. And that's a great start, but how can you get even more value out of your data? Predictive analytics allows you to anticipate future events, not just analyze the past.

Applications of Predictive Analytics
Predictive analytics is a methodology of data analysis telegram db used to forecast future events. This has become possible thanks to the advent of predictive technologies and is used in many areas of business, from planning the promotion of new products and services to optimizing work in production.

Artificial intelligence identifies the target audience and its needs based on the results of previous interactions, analyzes the behavior of different consumer segments and predicts their actions in the future. The same principle is used to create a portrait of a non-target or unreliable client who can bring nothing to the business except lost time. Representatives of such a segment are assigned a certain set of characteristics, according to which they will be automatically filtered out in the future.


Kim Murashov, CMO & Co-founder LoyaltyLab

Marketing today is personalized, contextual and dynamic. More and more often, its goal is not to attract customers, but to understand each specific user at the level of their desires and consumption context.

Predictive analytics in this case helps to anticipate the very context of customer needs and desires and determine the best way to deliver information across physical and digital touchpoints, while building a unique personalized approach.

Predictive analytics is based on artificial intelligence technology. It allows you to analyze a huge array of data and build predictive models based on it in seconds. Predictive analytics in marketing is used to optimize prices, plan purchases, develop new products, prevent customer churn and the formation of debts.

Imagine a world where manufacturers know who you are and what you want, and can deliver the product that best and most seamlessly meets your needs through the communication channels that are convenient for you. That's the world we live in now, where predictive advertising is a key method for solving marketing problems.


Maxim Tsukanov, Head of Client Analytics and CRM, SAS Russia/CIS

British energy company ScottishPower uses predictive analytics to assess the risk of non-payment of bills across its entire customer base of around 5 million people. The risk data obtained in advance allows the marketing department to work more effectively with customers who are likely to default on payments: to understand the reasons for their insolvency, to offer solutions and, thus, possibly, to avoid churn.

Another example from this company: knowing in advance what date the client pays the invoice, the company sends a reminder just before this date. This reduces the number of unnecessary letters, increases customer loyalty and saves money on sending.

What about internet marketing? Predictive analytics in digital, promotions, advertising platforms, information resources and social networks - everywhere where the client can see your product in one way or another.

We talked to experts in the field of predictive analytics and found out why predictive analytics is necessary for an internet marketer and what benefits it brings to business.

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1. Attract more clients
Attracting customers is becoming increasingly difficult — we have a huge selection of online stores and services. In the fight for leads, the winning companies are those that can offer their audience an individual approach and real benefits. How do you know what will interest a particular customer?

Based on accumulated data about customers, the customer base is divided into segments depending on preferences, behavior, socio-demographic parameters and financial capabilities. Historical data allows you to build a predictive model and make the most accurate forecast: whether your offer will interest a specific customer or not.

Prioritizing leads by their readiness for a deal is called lead scoring. A great example of using scoring models is assessing a client's solvency, which is used in banks. The forecast is based on historical data, so if you are similar in some way to those who often take out loans and forget to repay them (for example, frequent job changes), the probability of refusal is quite high.

By determining the conversion potential of leads and dividing them into segments, you can make a targeted, and most importantly, the most relevant offer to the selected group of clients.


Maxim Tsukanov, Head of Client Analytics and CRM, SAS Russia/CIS

Our experience shows that with predictive customer analytics, the response to targeted marketing offers is much higher. Companies have incremental turnover, and their margins are growing. For example, this is what Azbuka Vkusa and the Pyaterochka and Perekrestok chains, which are part of X5 Retail, did. With the help of predictive analytics, they forecast the response to marketing offers and launch new advertising campaigns.

By segmenting potential buyers by their readiness for a deal, you can correctly target marketing messages through contextual, banner, and targeted advertising. This will increase the conversion of advertising activities and attract more customers.

2. Improve customer experience
Predictive analytics helps not only attract new customers, but also improve the customer experience of existing ones with personalized recommendations. For this, the clustering method is used.


Dmitry Zelenko, Commercial Director of Lanit Omni

Previously, it was common in marketing to segment customers by demographic, social and geographic characteristics. For example, "men under 55", "women 35+", etc. This is mainly a mechanical procedure. Clustering is a statistical method. It combines buyers into groups according to specific criteria - the most significant parameters of consumer behavior of buyers. With the help of clustering, the accuracy of personal offers built on the basis of data of this particular group increases.

How does the clustering method work in practice? First, communities of products that are often bought together are built. Here's how it looks in the example of sales analysis in a LEGO chain of stores: