Marketing Personalisation
Why use marketing personalisation?
Digital personalisation in marketing campaigns—or in other words personalised email newsletters, specific ads and offers tailored to individual users—is most probably one of the fastest emerging areas in targeted client marketing.
Did you know that companies utilising email personalisation are experiencing an increase in the overall sales revenue of up to 20% in comparison to non-personalised campaigns? In other words, if the annual revenue of the company from email campaigns is €1M then email personalisation can increase annual revenue by about €200k.
Use Case : Creating More Effective Marketing Campaigns
In this scenario the algorithm is being used to predict the best ways and times to deliver enhanced customer satisfaction.
Predicting: The algorithm identified opportunities to enhance customer satisfaction and revenue by predicting the products that are most relevant to the customer’s interest, allowing the business to promote offers that most closely meet the customers needs. This is achieved by reviewing up to 200 different parameters such as previous purchases, demographic information and combined with eth customers interaction history online, through call centres and by email.
Resolving: This approach minimises the chances of irritating customers with irrelevant products and services, while providing much better opportunities to generate revenue and add value to each customer relationship.
Result: Having a granular understanding of customer preferences and interests adds significant value to ERP and CRM systems, allowing the business to integrate data to deliver more targeted and effective personalized campaigns and landing pages. In turn this delivers a better response to marketing campaigns and delivers a higher ROI.
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About iERP
iERP’s mission is to provide an end-to-end business prediction platform with modules that address multiple business scenarios and ZERO required knowledge of artificial intelligence or machine learning technologies.
We are helping companies to predict sales and inventory demands, what their customers are going to purchase next, or identify customers that are going to be late with the payment.