Why Context and Memory Matter in Intelligent Marketing Assistants

The marketing team approaches the AI assistant with a task of writing a follow-up email to the prospect who visited the product demo. The AI assistant creates the email but repeats what has already been told and fails to address the client's concern regarding the cost of implementation.  

Although Intelligent Assistants have the capability of analyzing information about the campaign and giving recommendations, these answers will be disjointed because they do not have access to earlier communications. The AI Marketing Assistant requires memory and Contextual Intelligence to perform effectively.  

The following article provides an explanation of why this is so important.  

Pattern recognition allows AI to detect patterns of behavior within a dataset; however, pattern recognition doesn’t provide reasons for such behavior and what the marketers should do next. 

Contextual Intelligence provides the ability to understand the meaning of the information based on the campaign goal, buyer’s position in the sales funnel, and their previous interactions. 

In addition, contextual information will help AI Marketing Assistants relate one marketing activity to another. Content recommendation, lead nurturing emails, and campaign do not operate in isolation when talking about the same buyer group. Yet, this will depend on the data that intelligent assistants use.      

1. Disconnected Customer Interactions Weaken Personalization 

Understanding past contacts and the lead’s position in the sales cycle is necessary for marketers. In the absence of information on past engagements, an AI assistant can send a message disregarding the past conversation. 

A lead downloads a technical document after participating in a product webinar. An assistant without access to both interactions may recommend generic resource instead of suggesting a technical case study.   

2. Inconsistent Recommendations Create Additional Review 

In case an AI assistant forgets the reasoning that led to an initial recommendation, it will give recommendations that go against a strategy that was already in place.  

The AI assistant gives a recommendation for an industry-based campaign targeting enterprise accounts. During another session, it gives a recommendation for a general campaign targeting smaller accounts since it forgets the previous one.

3. Insufficient Context Results in Inefficient Campaign Execution 

In the lack of memory and Contextual Intelligence, the assistant will view each activity separately instead of seeing it as a part of the larger picture of the campaign. 

The marketing team changes its strategy based on the realization that the prospects are more responsive to implementation of messages than feature-based ones. The assistant that does not have knowledge of this decision will keep creating feature-laden emails.   

4. Insufficient Learning Limits the Benefits of Multiple Usage

When there is the same lack of understanding, marketers will not be able to use what has been done previously. 

Marketers conduct analysis of multiple campaigns and found that certain audience segment reacts to the customer evidence. Without the knowledge, they need to manually educate the AI assistant about this finding. The tool will continue to create content; however, the team will pay for that.    

1. Test Whether It Can Connect Information Across Interactions

AI Marketing Assistants need to leverage the right data from related marketing applications depending on permissions and integrations. 

An individual downloads a case study and then requests a demo. Let the assistant make a proposal for the next engagement. Is the suggestion considering all three activities or only the third activity?

What to evaluate: Test the assistant with situations where multiple sources of data are required. Is it capable of identifying important data and spotting missing information?

2. Assess Its Usage of Contextual Intelligence 

Contextual Intelligence assists in understanding customer relationship, campaign objectives, and business considerations.  

A prospect keeps visiting the pricing page even though they have expressed worries regarding implementation. The context-savvy assistant would suggest giving them implementation guide or conducting a consultation meeting.   

What to evaluate: Present the assistant with similar signals in different business contexts. Check how recommendations get changed accordingly.

3. Check If Memory Helps to Create Consistency 

Marketing managers should verify if assistants will be able to use past decisions without needing any constant corrections. 

The team decides that its future campaign will focus on creating measurable business results. The team asks the assistant to write an e-mail and a landing page. Does the assistant preserve the approved messaging direction across both assets?   

What to evaluate: Run multi-step tests over several sessions. Measure how often the assistant follows established instructions or requires marketers to repeat information.  

4. Look at Data Governance and Control of the Memories

An assistant that keeps outdated information or transfers the information from one client’s conversation to another raise’s privacy issues.   

A marketing assistant supports campaigns for two enterprise clients. It should be remembered that the information from one client should not influence the campaign of the other client. 

Questions to be asked: How is the vendor managing permission, data storage, data deletion, access control, data refreshing, and auditing.   

The future of marketing AI will depend on their ability to leverage knowledge from the past. However, there is the possibility of re-performing the same tasks even without knowing if these still work. The ultimate test for marketing AI assistants would be their ability to translate this knowledge into improved marketing decisions.