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AI REVOLUTIONIZING CONTEXT-AWARE MARKETING.

IMPLEMENTING AI IN CONTEXT-AWARE MARKETING.

AI REVOLUTIONIZING CONTEXT-AWARE MARKETING.

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AI REVOLUTIONIZING CONTEXT-AWARE MARKETING. | Neubrain | AI REVOLUTIONIZING CONTEXT-AWARE

Sometimes marketing emerges as one of the most important activities for the deaf, “throw an arrow in the dark”, “spray and pray” kind of activity, which drives its recipients crazy and executives invest in promotional activities to the point of despair. It’s no wonder that response costs tend to be below, and sellers need to think about what the promised vent hood and marketing aid are to help them meet quotas. 

 In several cases, however, the marketing is so compelling, personalized, and compelling that the product sells itself, and business leaders may be surprised if they want a sales group to do business. 

 What makes the difference? Two words: “customer context” 

 Understanding this context and adapting the content to the individual needs of this customer make the difference between the “noise” of advertising and “thank God you heard me, please inform me better”. 

 But how do you get the right experience in this context? The conventional solution has always been human intelligence. When you have a person focused on a particular customer, that character will analyze many of the customer’s wishes and pain factors so that each next ad can be on target all the time. 

 Needless to say, that technique doesn’t scale anymore. 

 The next high-quality solution, which is known to this day, is to summarize the not uncommon local pain factors of some clients and design “trips” that clients could position themselves on. Email trips, website trips, social platform trips – all are set up by setting the usual wishes and goals of customer segments. These trips can be effective if they are created with great care and attention, and address the customer’s most pressing questions. 

Can AI take things one step further?

The wonderful thing about all types of AI / ML strategies is that they can be designed to perceive behavior towards a customer and perceive patterns that give clues to the customer’s context. 

 For example, using cluster analysis to identify which behavioral variations occur together most often and associative analysis to discover patterns of consumption styles can begin to highlight key “moments” within the customer’s shopping journey. The succession of these styles on your shopping journey can give a familiar sense of how these “moments” perceive everything differently on these trips, allowing us to better understand where the customer is and consequently their unique context. These “moments” can be called “states” and the version can be driven by the idea of ​​”state machines”. Knowing the customer’s condition gives us on-site clues as to what kind of pleasure they are most likely to respond to: a significant improvement in the customer experience and a reduction in wasted advertising spend. 

 The “willingness to interact in an income conversation” fashions, on the other hand, is like a slanted version that reflects the customer’s previous history (novelty, frequency, purchase) with the company, current customer wishes, role of the company about unique services within the company’s industry and the client’s recent engagement with the company. These tilt modes, which carefully consider the context of the customer, tend to give noticeable warnings about the customer, avoiding the frustrations of “bloodless calls” or improperly scheduled calls. The mystery component is again the context of the customer. 

 Propensity to buy fashion is similarly closely related to radical 360-degree knowledge about the customer, their past purchases (and the possible restocking cycle coming up), aggressive positioning, dynamic initiatives on the site’s customer website, and more. The richer the client context, the higher the performance of the version. 

 Well, if the AI ​​version has been built, this is mild in the context of the client; the aggregate uptrend is also limited. For example, if you create a version of AI to choose who to target to send emails to, you can examine the historical “open rate” or “click-through rate” and identify audience members who are much more likely to receive open email. or click on one of each of their links. There is nothing wrong with version type, but it goes a long way towards choosing a target market that relies primarily on totally tactical action rather than installing enough client context in the version. 

The equivalent version will achieve advanced effects if it contains know-how where the customer is on their shopping journey, if there are lively customer-side initiatives, while generally using email to get acquainted with a company and more. The better you know the version about using an email through the application user context, the more powerful the version could be in attracting audiences that need to interact via email right now. 

 How to clean up, the use of using AI in marketing is the energy to bring context to marketing in a scalable and automatic way, and that makes the difference between a distant customer reaction and one where the boss says “YES! “

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