Predictive analyses are a tool increasingly used in marketing. Thanks to using Big Data, it is possible to move from retrospective analysis to real-time analysis and predictive analysis. The Predictive Marketing module allows you to predict your client’s future activities – making a purchase or resigning from your services, based on transactional and behavioural data according to the past and present client’s activities. Thanks to obtaining this data, you can adapt marketing activities to reduce the risk of losing customers, thereby increasing our company’s revenues.
Contents:
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Prediction value of purchase occurrence
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Prediction value of churn and CLV analysis occurrence
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Prediction value of purchase occurrence from source
PREDICTION VALUE OF PURCHASE OCCURRENCE
The purchase prediction module provides estimated data specifying the probability of a given contact making a purchase. The data are counted based on transactional data of the given contact and his visits to the website. The module is divided into 4 compartments, with an indication of the height of the probability value.
[1] Low – applies to values between 0 and 25%.
[2] Medium – includes values above 25% to 50%
[3] High– includes values above 50%, ending with 75%
[4] Very high – applies to values above 75% to 100%
[A] Minimal transaction value – the minimal value of the purchase made so far by the contact
[B] Maximal transaction value – the maximal value of the purchase made so far by the contact
[C] Average transaction value – the average value of transactions made so far by the contact
Data on the probability of purchase are presented in the upper part of the contact card and in the Predictive Analytics tab.
PREDICTION VALUE OF CHURN AND CLV ANALYSIS OCCURRENCE
The chart below presents the prediction value of the churn occurrence and the moment of making a purchase by the contact, recorded at a given time. The estimation of the probability of churn occurrence allows assessing the risk of the customer leaving due to not making another transaction.
This chart contains:
- historical data presenting how the customer’s churn probability developed in the past and how this translated into a later transaction;
- the current probability of customer churn;
- predicts the probability of customer churn over the next 60 days.
[1] CLV analysis – Customer Lifetime Value – the total value of products purchased by the contact.
[2] Total number of transactions – the total number of transactions made by the contact
[3] Prediction value of churn occurrence – the estimated risk of a customer leaving due to lack of re–transaction
[4] Purchase occurrence – indicates the moment of making a purchase by the contact
Below you will find contact details
[1] Date of the first transaction – first transaction made by the contact
[2] Date of the last transaction – the last transaction made by the contact
[3] Average time between transactions – the average value expressing the time of making the purchase by the contact, allows estimating when the customer will make the next purchase
[4] Time from the first transaction – the number of days from time of the first transaction by contact, until now
[5] Time since the last transaction – the number of days from the time of the last transaction made by the contact, until now
[6] Customer lifetime – time (calculated in days) between the first and the last transaction
PREDICTION VALUE OF PURCHASE OCCURRENCE FROM SOURCE
Specifies the probability of the transaction being made by the contact from the given source. Based on this analysis, you can choose which communication channel is the best to communicate with the customer since the probability of purchase is the highest.
[1] Recommendation frame [clicked] – making a purchase by clicking on a product in the recommendation frame
[2] Referral – making a purchase after entering the website from another website;
[3] Advert – making a purchase after entering the website by clicking on Google Ads;
[4] Search – making a purchase after entering the website by clicking on the link resulting from the search (in the search engine);
[5] Web Push from rule [clicked] – making a purchase after entering the website by clicking on the Web Push notification sent using automation rules;
[6] Mass SMS [sent] – making a purchase by entering the website after receiving a mass SMS;
[7] Mass Web Push [clicked] – making a purchase after entering the website by clicking on the mass Web Push notification;
[8] Email, as a rule [clicked] – making a purchase after entering the website by clicking on the email sent from the automation rules;
[9] UTM – making a purchase by entering the website from the link containing the UTM parameter;
[10] Mass email [clicked] – making a purchase after entering the website by clicking on an email sent in bulk;
[11] Web Push from rule [opened] – making a purchase by entering the website after opening the Web Push notification sent using automation rules;
[12] SMS usually [sent] – making a purchase by entering the website after receiving an SMS sent by means of automation rules;
[13] SMS from Workflow [sent] – making a purchase after receiving an SMS sent from Workflow;
[14] Workflow email [click] – making a purchase after clicking on the email sent from Workflow;
[15] Email – making a purchase after entering an email;
[16] Legend – the percentage value’s meaning in the context of the probability of a transaction occurring